The role

Nebius is looking for a Senior Data Scientist to build and deploy time series forecasting and classical machine learning models that support core business planning and operational decisions. You will own problems end-to-end, from data exploration and feature engineering to model deployment and monitoring, working with large-scale, real-world datasets.

The role emphasizes strong statistical thinking, robust modeling, and production reliability over experimentation with trendy frameworks. Your work will directly impact forecasting accuracy, resource planning, and key business metrics.

You’re welcome to work in our offices in Tel Aviv.

Your responsibilities will include: 

• Time series forecasting. Design, build, and maintain forecasting models (e.g., demand, usage, capacity). Evaluate performance using appropriate metrics and improve accuracy over time.
• Classical ML modeling. Develop and deploy models using regression,Binary classifcatiom ,  tree-based methods, and other well-established approaches where they provide the best trade-off between performance, interpretability, and maintainability.
• Feature engineering & data preparation. Build robust pipelines for data cleaning, transformation, and feature generation (including time-based features, seasonality, and external signals).
• Model evaluation & monitoring.Define evaluation frameworks, backtesting strategies, and monitoring to ensure models remain stable and reliable in production.
• Production & MLOps. Deploy models into production environments, collaborate on pipeline orchestration, and ensure reproducibility and version control.
• Exploratory analysis. Analyze large datasets to identify patterns, anomalies, and drivers of change in time-dependent behavior.
• Stakeholder collaboration. Work closely with business and product teams to define forecasting needs, align on success metrics, and translate model outputs into actionable insights.
• Cross-domain contribution. Contribute to adjacent data science areas (e.g., LLM-based systems) as needed, ensuring shared ownership and continuity across team responsibilities.

We expect you to have: 

• Experience as a data scientist working in production environments (5+ years).
• Experience with time series modeling and forecasting techniques.
• Experience building reliable data pipelines and working with large datasets.
• Experience with modern data science tools and ecosystems using Python (e.g., NumPy, Pandas, Scikit-learn, deep learning frameworks).
• Strong SQL skills and experience working with large datasets.
• Strong foundation in statistics and machine learning fundamentals.
• Background in Computer Science, Statistics, Mathematics, Industrial Engineering, Economics, or a related field (Master’s degree or equivalent).
• Demonstrated ability to take models from idea to production and ensure ongoing value.
• Demonstrated ability to apply structured thinking and robust evaluation in forecasting problems.
• Strong communication skills and ability to explain model outputs, uncertainty, and limitations.
• Working knowledge of spoken and written English.

It will be an added bonus if you have: 

• Experience working in cloud environments (preferably Azure).
• Experience with MLOps tools and production model lifecycle management.
• Experience with workflow orchestration tools such as Apache Airflow.
• Familiarity with LLM-based systems (e.g., evaluation, RAG pipelines) in applied settings.

As a Machine Learning Engineer at MyHeritage, you will play a crucial role in developing and implementing machine learning models that drive our products and user experiences. You will have a significant impact on our large and growing user base by leveraging the latest AI technologies to solve challenging problems at scale. This is a unique opportunity to join a forward-thinking team that is passionate about innovation, data, and machine learning.

What you’ll do

• Design, develop, and deploy machine learning models for various business needs.
• Collaborate with cross-functional teams, product managers, and software engineers, to integrate ML solutions into production systems.
• Experiment with and iterate on model architectures to improve accuracy, efficiency, and scalability.
• Work with large datasets to preprocess, clean, and extract meaningful features for model training.
• Utilize MLOps best practices to automate and streamline the development lifecycle of ML models.
• Monitor model performance and fine-tune for optimization.
• Stay updated with the latest research and advancements in machine learning and AI.

What you bring

Technical skills:

• Strong foundation in machine learning techniques such as supervised and unsupervised learning, deep learning, and vision/NLP experience.
• Proficiency in Python, along with popular ML libraries and frameworks such as PyTorch, Scikit-Learn, pandas, NumPy, SciPy, TensorFlow.
• Experience with data preprocessing and feature engineering for structured and unstructured data.
• Knowledge of model deployment and serving, using tools like Docker, Kubernetes, or cloud-based services (AWS, GCP, Azure).
• Familiarity with MLOps tools for automation and monitoring (e.g., MLFlow, ClearML).
• Experience with SQL for data extraction and analysis (optional).

Experience:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
• 7+ years of experience in software development, including at least 3 years of experience in the machine learning domains.
• Experience working in a B2C company or a similar high-paced environment is an advantage.
• Proven track record of implementing impactful machine learning solutions in production.

Core attributes

• Strong analytical mindset, with the ability to tackle complex problems with creativity and efficiency.
• Excellent communication skills for conveying technical ideas to both technical and non-technical stakeholders.
• High level of attention to detail and quality in your work.
• Ability to work effectively in a team environment, while also being a self-starter when working independently.
• Curious and eager to keep learning about the latest trends in AI and machine learning.
• Comfortable in a fast-paced, dynamic work environment with changing priorities.

 About MyHeritage

MyHeritage is the leading global discovery platform for exploring family history. With billions of historical records and family tree profiles, and with sophisticated matching technologies that work across all its assets, MyHeritage gives users the joy of discovering their past and empowering their future. MyHeritage is the most popular DNA test and family history service in Europe. Since 2020, MyHeritage is also home to the world’s most advanced AI technologies for repairing, enhancing, colorizing, and animating historical photos. Learn more at www.myheritage.com. See what it’s like to work at MyHeritage on www.glassdoor.com.

Benefits at MyHeritage

Our employees enjoy a range of benefits to enhance work-life balance and well-being:

• Hybrid work model
• Modern, beautiful office in Or Yehuda with shuttle service from Tel Aviv and employee parking (for our Or Yehuda employees)
• On-site gym and pilates classes
• Dog-friendly office
• Well-stocked kitchens and a monthly meal allowance 
• Fully funded supplemental health insurance

#LI-Hybrid

Job Description

Astera labs is seeking a skilled and motivated Data Scientist. This individual will play a pivotal role in identifying key data points for collection, developing strategies to accumulate data and deriving actionable insights an anomaly based on a solid foundation of relevant know-how. Also, will also be responsible for creating, testing, and deploying scripts and methods for data collection and analysis to support decision-making. The Engineer will collaborate with cross-functional teams to identify critical data sources to determine the most effective data collection strategies, will develop automated and scalable data collection pipelines, will ensure data quality, integrity, and consistency across all sources and may use AI techniques to refine the results toward failures predictions.

Basic Qualifications

• Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, or a related field.
• Advanced degrees in data science or Machine learning / AI - Advance.
• Proficiency in programming languages such as Python, R, or MATLAB.
• Strong understanding of data manipulation and analysis tools (e.g., Pandas, NumPy, SQL).
• Understanding of high speed interfaces such as Ethernet, PCI-E , WiFi.
• Experience with data visualization tools such as Tableau, Matplotlib, Graphana.
• Strong analytical and critical-thinking skills to identify patterns and outliers.
• Customer-obsession, Think and act with the customer in mind!
• Goal-driven, Self-motivated, be able to work independently and with teams with people around the globe.
• Entrepreneurial, open-minded behavior and can-do attitude.

Required Experience

• Experience with data manipulation and analysis tools (e.g., Pandas, NumPy, SQL).
• Machine learning and AI techniques and frameworks (e.g., TensorFlow, Scikit-learn).
• Proven ability to manage multiple tasks and meet deadlines.

Preferred Experience

• Embedded Firmware development with C-language, scripting with Python or other equivalent programming languages.
• Master’s degree in a relevant field.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) for data storage and processing.
• Familiarity with big data technologies (e.g., Hadoop, Spark).
• Knowledge of engineering design tools and processes.

About QuantHealth

QuantHealth is a growing AI startup in the clinical trial space, leveraging AI, biomedical data, knowledge graphs, and real-world patient data to simulate and optimize clinical trials for pharmaceutical companies. Our platform combines large-scale biomedical knowledge, clinical trial data, and patient-level data to help customers simulate clinical trials, reduce development risk and cost, shorten timelines, and improve the probability of clinical trial success.

About the Role

As a Data Scientist in the CDS R&D team, you will play a key role in researching, developing, and productionizing advanced data science solutions that power QuantHealth’s clinical simulation and prediction capabilities.

This role is designed for a strong applied data scientist who enjoys solving complex, ambiguous problems end-to-end: from data exploration and feature engineering, through model development and validation, to building scalable pipelines and AI-driven workflows. You will work closely with clinical experts, product, delivery, engineering, and other data science teams to translate challenging clinical and biomedical questions into robust data-driven solutions.

The ideal candidate brings several years of hands-on experience in applied data science, machine learning, and data pipeline development. Additional qualities we look at in candidates are scientific thinking, project ownership, and the ability to independently learn and operate in complex domains. Experience with biomedical or clinical data is a strong advantage.

This position involves working with a modern Python-based data science stack, building predictive models, developing data and ML pipelines, and contributing to the continuous improvement of QuantHealth’s clinical trial simulation product. The role requires curiosity, strong self-learning abilities, research orientation, and rigorous analytical thinking.

Responsibilities

• Design, develop, and validate data science models, algorithms, and tools that support QuantHealth’s internal and customer-facing clinical simulation products.
• Build end-to-end data science solutions, including data ingestion, feature engineering, model development, evaluation, and deployment-ready pipelines.
• Collaborate with clinical, product, delivery, engineering, and data science teams to prototype and implement data-driven solutions.
• Explore, process, and integrate diverse data sources, including clinical trial data, EHR/RWD, biomedical literature, biomedical ontologies, and knowledge graphs.
• Develop scalable ETL and data pipelines to support automated clinical simulation workflows.
• Build LLM-based and agentic systems to automate internal research, data processing, and biomedical reasoning workflows.
• Develop and improve data projects using tools such as PySpark, PyTorch, PyGeometric, MLflow, SQL, and the broader Python data stack.
• Stay current with emerging methodologies in machine learning, LLMs, knowledge graphs, biomedical AI, and applied data science.
• Contribute to internal knowledge sharing, best practices, and scientific discussions within the R&D organization.

Qualifications

• MSc or PhD in data science, computer science, computational biology, or a related quantitative field.
• 4+ years of hands-on experience in applied data science, machine learning, or algorithm development.
• Strong Python skills and practical experience with common data science and ML libraries such as pandas, NumPy, scikit-learn, PyTorch, SQL, and Spark.
• Proven experience building end-to-end data science solutions, from raw data processing to model development, evaluation, and implementation.
• Experience working with large, messy, real-world datasets and transforming them into reliable analytical or machine learning pipelines.
• Strong understanding of machine learning concepts, model evaluation, statistical analysis, and experimental design.
• Experience with cloud-based computing environments and scalable data processing pipelines.
• Strong analytical and problem-solving skills, with attention to details and scientific rigor.
• Strong research mindset and ability to independently learn new domains, methods, and technologies.
• Excellent communication skills and ability to collaborate effectively across clinical, product, engineering, and data science teams.

Strong Advantages

• Experience working with biomedical, healthcare (RWD/EHR), pharmaceutical, biomedical ontologies, or real-world patient data.
• Experience developing LLM-based workflows or agentic pipelines.
• Experience with knowledge graphs, graph embeddings, GNNs, or graph-based ML.
• Experience with PySpark, PyTorch, PyGeometric, MLflow, or similar ML engineering tools.
• Experience creating interactive data applications, dashboards, or internal tools.
• Familiarity with clinical trial design, drug development, computational/system biology, or translational medicine.

Job Summary

We're looking for a Data Analyst to join one of our R&D. You'll design and deliver data-driven solutions that combine data analysis, statistical methods, improved heuristics, and ML models training to power Beamup's autonomous supply chain intelligence platform. You'll work closely with data science, engineering, product, and supply chain experts — owning solutions from problem definition to iterative improvement.

Responsibilities

• Analyze large, multi-source operational datasets to identify trends, anomalies, and drivers of supply chain performance
• Improve and maintain heuristic rules and analytical models for supply chain use cases (e.g., inventory risk, demand patterns, root-cause analysis)
• Train and iterate on ML models to enhance existing analytical solutions
• Define success metrics and evaluation plans with support from senior DS/PM; run error analysis
• Work with stakeholders to create and maintain ground truth (label definitions, labeling workflows, QA checks, feedback loops)
• Implement and refine hybrid decision logic by combining analytical outputs with statistical methods and SQL/rules-based logic for robustness and explainability
• Collaborate with software engineers to support deployment of analytical solutions, including testing and logging
• Monitor deployed models/rules, investigate performance issues (data quality, drift, edge cases), and iterate based on outcomes
• Contribute to team practices: reproducible notebooks/code, documentation, and experiment tracking

Requirements

• BSc in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field (or equivalent practical experience)
• 2+ years of experience as a Data Analyst in a product or business environment (or equivalent practical experience)
• Familiarity with Python and data analysis workflows
• Solid SQL skills (joins, aggregations, window functions) and comfort working with production data in a warehouse/lake environment
• Experience performing rigorous evaluation (baselines, cross-validation where relevant, error analysis)
• Ability to translate business questions into measurable metrics and a clear analytical plan (with guidance when needed)
• Experience working with messy real-world data: data validation, debugging pipelines, and collaborating on labeling/ground truth
• Strong communication and collaboration skills with engineering, product, and domain experts; comfortable receiving feedback and iterating fast

Nice to Have (Advantages)

• Experience building predictive ML models (e.g., classification, regression, anomaly detection)
• Familiarity with common DS libraries: pandas, NumPy, scikit-learn
• Experience with big data tools (e.g., Spark, BigQuery, Snowflake, Databricks)
• Experience with development AI agents (Cursor, Caude-Code, etc.)

About Pagaya

Pagaya is a global technology company making life-changing financial products and services available to more people nationwide, as it reshapes the financial services ecosystem. By using machine learning, a vast data network and a sophisticated AI-driven approach, Pagaya provides comprehensive consumer credit and residential real estate solutions for its partners, their customers, and investors. Its proprietary API and capital solutions integrate into its network of partners to deliver seamless user experiences and greater access to the mainstream economy. Pagaya has offices in New York and Tel Aviv. For more information, visit pagaya.com.

About the Role

Pagaya is seeking an experienced data scientist to join our Model Validation team. The ideal candidate will bring a blend of data science knowledge and analytical skills, understanding in Finance and an understanding of the regulatory landscape of credit lending. As a core team member, you will work closely with model developers to assess model performance, data quality, conceptual soundness, and produce thorough, compliant documentation for regulatory review, ensuring our models meet the highest standards. You will be required to challenge the models, helping Pagaya deliver high-quality validated models for loan pricing and underwriting.

Key Responsibilities:

• Conduct thorough validation of ML models to assess performance, data quality, robustness, conceptual soundness, regulatory requirements and more
• Conduct independent tests to assess performance and limitations, and establish model assumptions
• Suggest and develop alternative models to challenge and improve existing models
• Provide insights into the' strengths and weaknesses of models in terms of Data Science methodology and Finance as well as recommend practical solutions.
• Prepare comprehensive validation documentation
• Support the ongoing monitoring and improvement of existing predictive models
• Stay updated on the latest research in ML credit risk modeling and financial news, ensuring that our practices align with industry standards

Required Qualifications:

• 3+ years of experience in Data Science roles with experience with ML models- Must
• Work experience in a financial company
• Python - experience with data science packages (pandas, numpy,  sklearn, seaborn)
• Bachelor's degree in Data Science, Statistics, Computer Science, or a related quantitative field
• Excellent problem-solving skills with the ability to think critically and creatively
• Strong communication skills and the ability to work effectively in a collaborative team environment
• Fluent in English, both verbal and written

Preferred Qualifications:

• Masters degree
• Experience in Finance, specifically with financial data or models - advantage
• Experience with cloud platforms (e.g., AWS) and distributed computing
• Familiarity with regulatory compliance requirements for financial models (e.g., SR 11-7)

Our Team

Pagaya was founded in 2016 by seasoned research, finance, and technology entrepreneurs, and we are now 500+ strong in New York, Los Angeles, and Tel Aviv.

We move fast and smart, identifying new opportunities and building end-to-end solutions from AI models and unique data sources. Every Pagaya team member is solving new and exciting challenges every day in a culture based on partnership, collaboration, and community.

Join a team of builders who are working every day to enable better outcomes for our partners and their customers.

Our Values

Our values are at the heart of everything we do. We believe great solutions are built through a great community.

• Advance Inclusion- We create a world where everyone can win, designing systems that better represent people and generate sustainable value for our employees, partners and investors.
• Be Accountable Together- We proudly own our actions and our results, taking initiative to ensure our work gets over the finish line as a team.
• Continuously Learn- We challenge ourselves for the sake of getting better as individuals, as teams, and as an organization to deliver for our partners.
• Debate and Commit- We respectfully and openly debate to strengthen our ideas and build shared conviction - once we decide, we go all in, together.
• Dream Big and Act- We boldly tackle complex problems, pressure-test solutions in real-time, and adapt with speed and energy.

More than just a job

We believe health, happiness, and productivity go hand-in-hand. That's why we're continually looking to enhance the ways we support you with benefits programs and perks that allow every Pagayan to do the best work of their life. 

Pagaya is an equal opportunity employer. Pagaya is encouraging diversity and actively seeking applicants from all backgrounds, as are committed to creating a diverse workforce together with an inclusive environment for all. Employment is decided on the basis of qualifications, skills, and business needs.

Pagaya is an equal opportunity employer. Pagaya is encouraging diversity and actively seeking applicants from all backgrounds, as are committed to creating a diverse workforce together with an inclusive environment for all. Employment is decided on the basis of qualifications, skills, and business needs.

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Eligible locations:

• USA
• Canada
• UK
• Portugal
• Spain
• France
• Italy
• Germany
• Netherlands
• Poland
• Czech republic
• Israel
• UAE

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Eligible locations:

• USA
• Canada
• UK
• Portugal
• Spain
• France
• Italy
• Germany
• Netherlands
• Poland
• Czech republic
• Israel
• UAE

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Eligible locations:

• USA
• Canada
• UK
• Portugal
• Spain
• France
• Italy
• Germany
• Netherlands
• Poland
• Czech republic
• Israel
• UAE

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Nift is disrupting performance marketing, delivering millions of new customers to brands every month. We’re actively looking for a hands-on Senior Data Scientist to focus on building ML models for scalable consumer-based services (or non-consumer services that require highly scalable ML models for predictions and classifications).

As a Senior Data Scientist, you’ll report to the Data Science Manager and work closely with both our Data Science and Engineering teams. You will play a crucial role in developing models for complex parts of our business. You will analyze large datasets, identify patterns, and develop a variety of model types. You’ll also contribute to the improvement and re-design of our current models. You will collaborate closely with cross-functional teams to translate insights into actionable solutions. This is a hands-on position where you will apply your expertise in statistical modeling, machine learning, and data mining techniques.

This role is ideally based in Israel, but strong candidates from the U.S. will also be considered.

Our Mission: 

Nift’s mission is to reshape how people discover and try new brands by introducing them to new products and services through thoughtful "thank-you" gifts. Our customer-first approach ensures businesses acquire new customers efficiently while making customers feel valued and rewarded.

We are a data-driven, cash-flow-positive company that has experienced 731% growth over the last three years. Now, we’re scaling to become one of the largest sources for new customer acquisition worldwide. Backed by investors who supported Fitbit, Warby Parker, and Twitter, we are poised for exponential growth and ready to demonstrate impact on a global scale. Read more about our growth here.

What you will do:

• Data Analysis and Exploration: You will need to explore and analyze large volumes of data to gain insights and identify patterns relevant to your modeling objectives. This involves data cleaning, preprocessing, and transforming data into a suitable format for modeling
• Model Development: You will design and develop models using statistical and machine-learning techniques. This includes selecting appropriate algorithms, feature engineering, model training, and evaluation
• Data Preparation: You will be responsible for preparing the data required for modeling, including gathering and integrating data from various sources, ensuring data quality and consistency, and defining appropriate features and variables
• Model Evaluation and Testing: You will assess the performance and accuracy of the models using appropriate evaluation metrics. This includes conducting experiments, cross-validation, and measuring the effectiveness of recommendations
• Optimization and Tuning: You will fine-tune models to optimize their performance, improve accuracy, reduce bias or overfitting, and enhance the efficiency of the algorithms. provide actionable recommendations
• Analyze large datasets to identify patterns, trends, and insights that can be leveraged to improve business performance
• Design, build and evaluate systems to personalize consumer experience and drive customer engagement
• Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions
• Conduct rigorous testing and validation of models to ensure their accuracy, robustness, and reliability
• Monitor model performance, identify areas of improvement, and continuously refine models based on new data and evolving business needs
• Stay up-to-date with the latest advancements in data science, machine learning, and recommendation system technologies, and apply them to solve business challenges 

What You Need:

• Master's or Ph.D. in a quantitative field such as Computer Science, Statistics, Applied Mathematics, Economics, Physics, or a related discipline
• 5+ years of experience working in a professional setting deploying models
• Strong experience in building and deploying into production predictive models and recommendation systems using statistical modeling, machine learning, and data mining techniques
• Proficiency in Machine Learning: You should have a deep experience with various machine learning techniques, such as regression, classification, clustering, dimensionality reduction, and ensemble methods. Familiarity with popular algorithms like decision trees, random forests, Boosted trees, and regularized regression.
• Experience with match-propensity models, embeddings based models, cold-start handling, calibration/post-processing, integrating models into ad-tech workflows and business logic, measuring model performance within ad-tech systems.
• Strong Background in Statistics and Mathematics: A solid foundation in statistical concepts, linear algebra, calculus, and probability theory is essential for understanding the principles behind machine learning algorithms and recommendation systems
• Proficiency in programming languages such as Python or R, along with experience in data manipulation and analysis using libraries like NumPy, Pandas, or SciPy
• Solid understanding of data preprocessing, feature engineering, and model evaluation techniques
• Experience in ad-tech is a must, media/advertising platforms, demand-side platforms and supply-side platforms in digital advertising or retail-media networks
• Familiarity with big data technologies and distributed computing frameworks (e.g., Hadoop, Spark) is a plus
• Problem-solving skills and the ability to think critically to develop innovative solutions
• Excellent communication and collaboration skills to effectively work with cross-functional teams and present complex findings

What you get: 

• Competitive compensation, flexible remote work
• Unlimited Responsible PTO
• Great opportunity to join a growing, cash-flow-positive company while having a direct impact on Nift's revenue, growth, scale, and future success