Tucows (NASDAQ:TCX, TSX:TC) is possibly the biggest Internet company you’ve never heard of. We started as a simple shareware site in 1993 and have since grown into a stable of businesses: Tucows Domains, Ting Internet and Wavelo.

We embrace a people-first philosophy that is rooted in respect, trust, and flexibility. We believe that whatever works for our employees is what works best for us. It’s also why the majority of our roles are remote-first, meaning you can work from anywhere you can connect to the Internet!

Today, over one thousand people work in over 20 countries to help us make the Internet better. If this sounds exciting to you, join the herd! 

About the opportunity

We are seeking an experienced and versatile Analytics Engineer to join our dynamic team. In this role, you will apply your advanced analytics expertise to extract actionable insights from raw data. The ideal candidate will have a strong background in data engineering, analytics, and machine learning, with the ability to drive data-driven decision-making across the organization. 

What You Will Be Doing

• Data Modeling & Pipelines: Design, develop, and maintain complex data models in our Snowflake data warehouse. Utilize dbt (Data Build Tool) to create efficient data pipelines and transformations for our data platform.
  
  
• Snowflake Intelligence Integration: Leverage Snowflake Intelligence features (e.g., Cortex Analyst, Cortex Agents, Cortex Search, AISQL) to implement conversational data queries and AI-driven insights directly within our data environment. Develop AI solutions that harness these capabilities to extract valuable business insights.
  
  
• Advanced SQL & Analysis: Design and build advanced SQL queries to retrieve and manipulate complex data sets. Dive deep into large datasets to uncover patterns, trends, and opportunities that inform strategic decision-making.
  
  
• Business Intelligence (BI): Develop, maintain, and optimize Looker dashboards and LookML to effectively communicate data insights. Leverage Looker’s conversational analytics and data agent features to enable stakeholders to interact with data using natural language queries.
  
  
• Cross-Functional Collaboration: Communicate effectively with stakeholders to understand business requirements and deliver data-driven solutions. Identify opportunities for implementing AI/ML/NLP technologies in collaboration with product, engineering, and business teams.
  
  
• Programming & Automation: Write efficient Python code for data analysis, data processing, and automation of recurring tasks. Skilled in shell scripting and command-line tools to support data workflows and system tasks. Ensure code is well-tested and integrated into automated workflows (e.g., via Airflow job scheduling).
  
  
• Visualization & Presentation: Create compelling visualizations and presentations to deliver analytical insights and actionable recommendations to senior management and cross-functional teams. Tailor communication of complex analyses to diverse audiences.
  
  
• Innovation & Best Practices: Stay up-to-date with industry trends, emerging tools, and best practices in data engineering and analytics (with a focus on dbt features, Snowflake’s latest offerings and BI innovations). Develop and implement innovative ideas to continuously improve our analytics stack and practices.

What You Bring

• Education: Bachelor’s degree in Computer Science, Statistics, or a related field; Master’s degree preferred.
  
  
• Experience: 2+ years of experience in data analytics or a related field, with significant exposure to AI and Machine Learning applications in analytics.
  
  
• SQL Expertise: Advanced SQL skills with experience in writing and optimizing complex queries on large-scale datasets.
  
  
• dbt Proficiency: Hands-on experience with dbt (Data Build Tool) and its features for building, testing, and documenting data models.
  
  
• Data Modeling: Expert-level knowledge of data modeling and data warehouse concepts (e.g., star schema, normalization, slowly changing dimensions).
  
  
• Snowflake & AI Capabilities: Experience with Snowflake’s Data Cloud platform and familiarity with its advanced AI capabilities (Snowflake Intelligence – Cortex Analyst, Cortex Agents, Cortex Search, AISQL, etc.) is highly preferred.
  
  
• Business Intelligence Tools: Strong skills in Looker data visualization and LookML (including familiarity with Looker’s conversational AI and data agent capabilities) or similar BI tools.
  
  
• AI Agents & Automation: Experience with AI agents or generative AI tools to optimize workflows and service delivery (such as creating chatbots or automated analytic assistants) is a plus.
  
  
• Real-Time & Streaming Data: Experience with real-time data processing and streaming technologies (e.g., Kafka, Kinesis, Spark Streaming) for handling continuous data flows.
  
  
• Programming: Proficient in Python for data analysis and manipulation (pandas, NumPy, etc.), with the ability to write clean, efficient code. Experienced with shell scripting and command-line tools for automating workflows and data processing tasks.
  
  
• ETL/Orchestration: Familiarity with ETL processes and workflow orchestration tools like Apache Airflow (or similar scheduling tools) for automating data pipelines alongside Docker for local development and testing.
  
  
• Cloud Platforms: Experience with cloud platforms and services (especially AWS or GCP) for data storage, compute, and deployment.
  
  
• Version Control & CI/CD: Solid understanding of code versioning (Git) and continuous integration/continuous deployment (CI/CD) processes in a data engineering context.
  
  
• Agile Methodology: Familiarity with agile development methodologies and ability to work in a fast-paced, iterative environment.
  
  
• Soft Skills: Excellent communication and presentation skills, with critical thinking and problem-solving abilities. Proven track record of working effectively on cross-functional teams and translating business needs into technical solutions.
  
  
• Data Governance & Ethics: Experience implementing data governance best practices, ensuring data quality and consistency. Knowledge of data ethics, bias mitigation strategies, and data privacy regulations (e.g., GDPR, CCPA) with a commitment to compliance.

Nice to Have

• Community & Open Source: Contributions to open-source projects or active participation in data community initiatives.
  
  
• AI/ML Skills: Experience with applying Artificial Intelligence/Machine Learning techniques in analytics (e.g., building predictive models for forecasting, churn prediction, fraud detection, etc.). Practical experience deploying models and using MLOps/DataOps practices for lifecycle management.
  
  
• Statistical Background: Solid foundation in statistics and probability, with ability to apply various modeling techniques and design A/B tests or experiments.
  
  
• Additional Programming: Knowledge of additional programming or query languages (e.g., R, Scala, Julia, Spark SQL) that can be applied in analytics workflows.
  
  
• Certifications: Certifications in relevant data technologies or cloud platforms (such as Snowflake, AWS, GCP, or Looker) demonstrating your expertise.

The ideal candidate will be a self-starter with a passion for data and analytics, capable of navigating complex datasets to uncover valuable insights. They should be comfortable working in a fast-paced environment, adapting to new technologies, and driving innovation in our data practices.They should be able to navigate complex data landscapes, uncover meaningful insights, and communicate these findings effectively to both technical and non-technical audiences. The ability to stay current with industry trends and continuously learn new technologies is essential in this role.

This role offers the opportunity to make a significant impact on our organization's data strategy and contribute to critical business decisions through advanced analytics. The successful candidate will play a key role in shaping our data culture and driving the adoption of cutting-edge data technologies and methodologies.

If you are a data enthusiast with a track record of delivering impactful analytics solutions and a desire to push the boundaries of what's possible with data, we want to hear from you!

​​The base salary range for this position is $81,990 to $91,100. Range shown in $CAD for Canadian residents. Other countries will differ. Range may vary on a number of factors including, but not limited to: location, experience and qualifications. Tucows believes in a total rewards offering that includes fair compensation and generous benefits. 

Want to know more about what we stand for? At Tucows we care about protecting the open Internet, narrowing the digital divide, and supporting fairness and equality.

We also know that diversity drives innovation. We are committed to inclusion across race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability status. We celebrate multiple approaches and diverse points of view.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.

We use AI-enabled tools throughout our recruitment process to help us work more efficiently and consistently. These tools support our hiring teams by organizing and reviewing information, while final hiring decisions are always made by people.

Tucows and its subsidiaries participate in the E-verify program for all US employees.

Learn more about Tucows, our businesses, culture and employee benefits on our site here.

Tucows (NASDAQ:TCX, TSX:TC) is possibly the biggest Internet company you’ve never heard of. We started as a simple shareware site in 1993 and have since grown into a stable of businesses: Tucows Domains, Ting Internet and Wavelo.

We embrace a people-first philosophy that is rooted in respect, trust, and flexibility. We believe that whatever works for our employees is what works best for us. It’s also why the majority of our roles are remote-first, meaning you can work from anywhere you can connect to the Internet!

Today, over one thousand people work in over 20 countries to help us make the Internet better. If this sounds exciting to you, join the herd! 

About the opportunity

We are seeking an experienced and versatile Analytics Engineer to join our dynamic team. In this role, you will apply your advanced analytics expertise to extract actionable insights from raw data. The ideal candidate will have a strong background in data engineering, analytics, and machine learning, with the ability to drive data-driven decision-making across the organization. 

What You Will Be Doing

• Data Modeling & Pipelines: Design, develop, and maintain complex data models in our Snowflake data warehouse. Utilize dbt (Data Build Tool) to create efficient data pipelines and transformations for our data platform.
  
  
• Snowflake Intelligence Integration: Leverage Snowflake Intelligence features (e.g., Cortex Analyst, Cortex Agents, Cortex Search, AISQL) to implement conversational data queries and AI-driven insights directly within our data environment. Develop AI solutions that harness these capabilities to extract valuable business insights.
  
  
• Advanced SQL & Analysis: Design and build advanced SQL queries to retrieve and manipulate complex data sets. Dive deep into large datasets to uncover patterns, trends, and opportunities that inform strategic decision-making.
  
  
• Business Intelligence (BI): Develop, maintain, and optimize Looker dashboards and LookML to effectively communicate data insights. Leverage Looker’s conversational analytics and data agent features to enable stakeholders to interact with data using natural language queries.
  
  
• Cross-Functional Collaboration: Communicate effectively with stakeholders to understand business requirements and deliver data-driven solutions. Identify opportunities for implementing AI/ML/NLP technologies in collaboration with product, engineering, and business teams.
  
  
• Programming & Automation: Write efficient Python code for data analysis, data processing, and automation of recurring tasks. Skilled in shell scripting and command-line tools to support data workflows and system tasks. Ensure code is well-tested and integrated into automated workflows (e.g., via Airflow job scheduling).
  
  
• Visualization & Presentation: Create compelling visualizations and presentations to deliver analytical insights and actionable recommendations to senior management and cross-functional teams. Tailor communication of complex analyses to diverse audiences.
  
  
• Innovation & Best Practices: Stay up-to-date with industry trends, emerging tools, and best practices in data engineering and analytics (with a focus on dbt features, Snowflake’s latest offerings and BI innovations). Develop and implement innovative ideas to continuously improve our analytics stack and practices.

What You Bring

• Education: Bachelor’s degree in Computer Science, Statistics, or a related field; Master’s degree preferred.
  
  
• Experience: 2+ years of experience in data analytics or a related field, with significant exposure to AI and Machine Learning applications in analytics.
  
  
• SQL Expertise: Advanced SQL skills with experience in writing and optimizing complex queries on large-scale datasets.
  
  
• dbt Proficiency: Hands-on experience with dbt (Data Build Tool) and its features for building, testing, and documenting data models.
  
  
• Data Modeling: Expert-level knowledge of data modeling and data warehouse concepts (e.g., star schema, normalization, slowly changing dimensions).
  
  
• Snowflake & AI Capabilities: Experience with Snowflake’s Data Cloud platform and familiarity with its advanced AI capabilities (Snowflake Intelligence – Cortex Analyst, Cortex Agents, Cortex Search, AISQL, etc.) is highly preferred.
  
  
• Business Intelligence Tools: Strong skills in Looker data visualization and LookML (including familiarity with Looker’s conversational AI and data agent capabilities) or similar BI tools.
  
  
• AI Agents & Automation: Experience with AI agents or generative AI tools to optimize workflows and service delivery (such as creating chatbots or automated analytic assistants) is a plus.
  
  
• Real-Time & Streaming Data: Experience with real-time data processing and streaming technologies (e.g., Kafka, Kinesis, Spark Streaming) for handling continuous data flows.
  
  
• Programming: Proficient in Python for data analysis and manipulation (pandas, NumPy, etc.), with the ability to write clean, efficient code. Experienced with shell scripting and command-line tools for automating workflows and data processing tasks.
  
  
• ETL/Orchestration: Familiarity with ETL processes and workflow orchestration tools like Apache Airflow (or similar scheduling tools) for automating data pipelines alongside Docker for local development and testing.
  
  
• Cloud Platforms: Experience with cloud platforms and services (especially AWS or GCP) for data storage, compute, and deployment.
  
  
• Version Control & CI/CD: Solid understanding of code versioning (Git) and continuous integration/continuous deployment (CI/CD) processes in a data engineering context.
  
  
• Agile Methodology: Familiarity with agile development methodologies and ability to work in a fast-paced, iterative environment.
  
  
• Soft Skills: Excellent communication and presentation skills, with critical thinking and problem-solving abilities. Proven track record of working effectively on cross-functional teams and translating business needs into technical solutions.
  
  
• Data Governance & Ethics: Experience implementing data governance best practices, ensuring data quality and consistency. Knowledge of data ethics, bias mitigation strategies, and data privacy regulations (e.g., GDPR, CCPA) with a commitment to compliance.

Nice to Have

• Community & Open Source: Contributions to open-source projects or active participation in data community initiatives.
  
  
• AI/ML Skills: Experience with applying Artificial Intelligence/Machine Learning techniques in analytics (e.g., building predictive models for forecasting, churn prediction, fraud detection, etc.). Practical experience deploying models and using MLOps/DataOps practices for lifecycle management.
  
  
• Statistical Background: Solid foundation in statistics and probability, with ability to apply various modeling techniques and design A/B tests or experiments.
  
  
• Additional Programming: Knowledge of additional programming or query languages (e.g., R, Scala, Julia, Spark SQL) that can be applied in analytics workflows.
  
  
• Certifications: Certifications in relevant data technologies or cloud platforms (such as Snowflake, AWS, GCP, or Looker) demonstrating your expertise.

The ideal candidate will be a self-starter with a passion for data and analytics, capable of navigating complex datasets to uncover valuable insights. They should be comfortable working in a fast-paced environment, adapting to new technologies, and driving innovation in our data practices.They should be able to navigate complex data landscapes, uncover meaningful insights, and communicate these findings effectively to both technical and non-technical audiences. The ability to stay current with industry trends and continuously learn new technologies is essential in this role.

This role offers the opportunity to make a significant impact on our organization's data strategy and contribute to critical business decisions through advanced analytics. The successful candidate will play a key role in shaping our data culture and driving the adoption of cutting-edge data technologies and methodologies.

If you are a data enthusiast with a track record of delivering impactful analytics solutions and a desire to push the boundaries of what's possible with data, we want to hear from you!

​​The base salary range for this position is $81,990 to $91,100. Range shown in $CAD for Canadian residents. Other countries will differ. Range may vary on a number of factors including, but not limited to: location, experience and qualifications. Tucows believes in a total rewards offering that includes fair compensation and generous benefits. 

Want to know more about what we stand for? At Tucows we care about protecting the open Internet, narrowing the digital divide, and supporting fairness and equality.

We also know that diversity drives innovation. We are committed to inclusion across race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability status. We celebrate multiple approaches and diverse points of view.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.

We use AI-enabled tools throughout our recruitment process to help us work more efficiently and consistently. These tools support our hiring teams by organizing and reviewing information, while final hiring decisions are always made by people.

Tucows and its subsidiaries participate in the E-verify program for all US employees.

Learn more about Tucows, our businesses, culture and employee benefits on our site here.

Who you are:

We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking a Data Scientist Intern within the Insights & Integrations Consultancy team that will support ongoing projects by assisting in data analysis, visualization, and the development of solutions that help optimize fleet operations and enhance customer decision-making. If you love technology and are keen to join an industry leader — we would love to hear from you!

What you'll do:

As a Data Scientist Intern within the Insights & Integrations Consultancy team, you will gain hands-on experience in leveraging data to drive strategic insights for customers and partners. In this role, you will have the opportunity to work with Geotab’s open data platform, learning how to explore and interpret telematics data to solve real-world business challenges. You will collaborate with cross-functional teams, including Data Scientists, Product Managers, and Integration Engineers, to support key initiatives focused on portfolio expansion, retention, and solution innovation.

Throughout your internship, you will develop a deeper understanding of industry trends, fleet analytics, and Geotab’s ecosystem, while contributing to meaningful projects that have a direct impact on customers and partners.

The opportunity:

• 12 month work-term beginning September 2026.
• Full-time, paid internship: Monday - Friday, 37.5hrs/week.
• Your first week at Geotab begins with 'GEO Launch' - a one-week Employee Orientation. Click here to learn more!
• Learn more about the Geotab Campus Program here.
• This posting is for an existing vacancy.

How you'll make an impact:

• Develop foundational knowledge of the Geotab ecosystem and telematics data, assisting in data exploration, visualization, and analysis to support customer and partner insights.
• Collaborate with Data Scientists and cross-functional teams to optimize the use of Geotab’s technology, providing analytical support for ongoing projects.
• Assist in building and refining data-driven dashboards for internal and external stakeholders, facilitating informed decision-making.
• Support continuous improvement initiatives by gathering feedback, identifying trends, and contributing to data-driven recommendations.
• Work closely with mentors to apply statistical and machine learning techniques in fleet analytics, helping develop predictive models and insights under guidance.
• Contribute to proof-of-concept development, assisting in the creation of small-scale models or prototypes that demonstrate the feasibility and impact of analytics-driven solutions.
• Stay informed on industry trends, emerging data science techniques, and best practices to enhance analytical capabilities.
• Maintain and update documentation on data analysis processes, methodologies, and findings for internal knowledge-sharing.
• Assist in maintaining data pipelines using SQL and Python-based workflows to support ongoing analytics initiatives.

What you'll bring to the role:

The ML Platform Engineering team at Afresh is responsible for building and maintaining the foundational infrastructure and tooling that powers all of our machine learning and applied science solutions. We provide the shared components and services that enable our teams to develop, deploy, and scale robust ML models. This includes a performant data API, configurable featurization, reliable forecasting systems, highly parallel optimization engines, and scalable training pipelines, and deep experimentation capabilities. As our product suite and customer base grow, so does the scale and complexity of what our platform needs to support, gracefully accommodating predictions and simulations across various time scales (hours, days, weeks), complex data hierarchies (pallets on a truck, shelves of mangos in a store, chunks of fruit in a bowl), and endless configuration possibilities (average shelf fullness, backroom loads, truck capacities).

About the Role

As a Staff ML Platform Engineer on the ML Platform Engineering team, you will be instrumental in elevating our core ML platform to its next level of performance, reliability, and scalability. You'll work on the critical infrastructure that directly enables all of Afresh's Machine Learning and Applied Science teams to innovate faster and deliver impact. Your contributions will empower our product suite, including our flagship Prediction Engine, to power replenishment decisions on more than 15% of all produce sold in the United States.

What You'll Do:

• In your first 3 months, you'll partner with ML, Applied Science, and engineering leadership to identify the highest-leverage gaps in our ML platform and shape a multi-quarter technical strategy to close them.
• By the end of your first 6 months, you will have driven a platform-level initiative that meaningfully changes what's possible at Afresh — establishing the architecture for real-time inference across the company, redesigning model configuration and deployment end-to-end, or rebuilding our distributed inference layer for an order-of-magnitude growth in scale — while raising the bar across the ML org through design reviews and mentorship of other senior engineers.

Skills and Experience

• BS in Computer Science or a relevant technical field.
• 7+ years of professional software development experience with a proven track record of shipping high-quality applications and services.
• Experience working collaboratively with machine learning engineers, data scientists, or applied scientists on large-scale software projects involving machine learning models.
• You possess a genuine curiosity about ML modeling (e.g., demand forecasting, state estimation, ordering policy). You aren't just building "pipes"; you want to understand what is flowing through them. 
• You have a deep understanding of how scientists work and build tools that bridge the gap between a research notebook and production-grade software. 
• Technical leadership experience and a demonstrated ability to mentor junior engineers.
• Deep expertise in library design, API design, data structures, and algorithms.
• Strong familiarity with the Python ecosystem (NumPy, Pandas, Torch, PySpark). While our stack is Python-heavy, we value engineers who are stack-agnostic and focus on solving fundamental distributed systems problems. 
• Proven ability to architect high-throughput distributed inference systems using Spark, Dask, or Ray. 
• Experience in engineering robust data architectures with a focus on schema evolution and performance tuning. 
• Experience with end-to-end orchestration (lineage tracking, resource scheduling, self-healing pipelines).

Tech Stack: Our backend is pure Python (NumPy, Pandas, Torch, PySpark, Cython, orchestrated in Airflow). We use Databricks as our data warehouse. While we'd like you to have very good familiarity with Python, many of our problems are stack-agnostic.

This position is not eligible for company sponsorship.

Salary Band in Canada (CAD): $148,000 - $242,000

The ML Platform Engineering team at Afresh is responsible for building and maintaining the foundational infrastructure and tooling that powers all of our machine learning and applied science solutions. We provide the shared components and services that enable our teams to develop, deploy, and scale robust ML models. This includes a performant data API, configurable featurization, reliable forecasting systems, highly parallel optimization engines, and scalable training pipelines, and deep experimentation capabilities. As our product suite and customer base grow, so does the scale and complexity of what our platform needs to support, gracefully accommodating predictions and simulations across various time scales (hours, days, weeks), complex data hierarchies (pallets on a truck, shelves of mangos in a store, chunks of fruit in a bowl), and endless configuration possibilities (average shelf fullness, backroom loads, truck capacities).

About the Role

As a Senior ML Platform Engineer on the ML Platform Engineering team, you will be instrumental in elevating our core ML platform to its next level of performance, reliability, and scalability. You'll work on the critical infrastructure that directly enables all of Afresh's Machine Learning and Applied Science teams to innovate faster and deliver impact. Your contributions will empower our product suite, including our flagship Prediction Engine, to power replenishment decisions on more than 15% of all produce sold in the United States.

What you will do:

• In your first 3 months, you might deliver a project that helps generalize model configuration, enables no-code model deploys for our various ML solutions, or vastly improves integration testing across our ML systems.
• By the end of your first 6 months, you will have owned the design and implementation of significant scalability improvements and additions to our ML platform. This might include new feature pipelines that power our recommendation engine, or work to stand up the first instance of real-time inference at Afresh.

Skills and Experience

• BS in Computer Science or a relevant technical field.
• 5+ years of professional software development experience with a proven track record of shipping high-quality applications and services.
• Experience working collaboratively with machine learning engineers, data scientists, or applied scientists on large-scale software projects involving machine learning models.
• You possess a genuine curiosity about ML modeling (e.g., demand forecasting, state estimation, ordering policy). You aren't just building "pipes"; you want to understand what is flowing through them. 
• You have a deep understanding of how scientists work and build tools that bridge the gap between a research notebook and production-grade software. 
• Technical leadership experience and a demonstrated ability to mentor junior engineers.
• Deep expertise in library design, API design, data structures, and algorithms.
• Strong familiarity with the Python ecosystem (NumPy, Pandas, Torch, PySpark). While our stack is Python-heavy, we value engineers who are stack-agnostic and focus on solving fundamental distributed systems problems. 
• Proven ability to architect high-throughput distributed inference systems using Spark, Dask, or Ray. 
• Experience in engineering robust data architectures with a focus on schema evolution and performance tuning. 
• Experience with end-to-end orchestration (lineage tracking, resource scheduling, self-healing pipelines).

Tech Stack: Our backend is pure Python (NumPy, Pandas, Torch, PySpark, Cython, orchestrated in Airflow). We use Databricks as our data warehouse. While we'd like you to have very good familiarity with Python, many of our problems are stack-agnostic.

This position is not eligible for company sponsorship.

Salary Range for Canada in CAD: $137,000 - $205,000

Narvar is growing! We are hiring a Senior Software Engineer II to build new products and improve all aspects of the Narvar platform. Data is at the core of our competitive advantage so the work you do has a large impact across the company, our business partners, and the lives of our end users.

Machine Learning Engineers at Narvar work across the stack. We are ‘full-stack’ ML Engineers. This means that we write production-level code and own machine learning operations for our teams. 

Day-to-day

• Design and deploy machine learning algorithms for use cases spanning e-commerce, consumer trends, markets, logistics, and new products
• Work on real-world consumer data for natural language processing, image classification, time series analysis, outlier detection, user modeling, etc
• Work with large unstructured data
• Be at the intersection of mathematics, machine learning, business, and computer science and impact millions of users through your work
• Multiply the effect of your data science and data team members by building frameworks, tools, and methodologies that the entire team use 
• Provide thought leadership to a team through high quality write-ups, reviews, and a strong vision grounded in practical experience and a wider industry view

What we’re looking for

• Strong machine learning skills. 
• Fluency in Python, PyTorch, Tensorflow, Pandas, numpy, and machine learning packages
• Data Engineering skills and large data experience. You should have dealt with large amounts of data (TB) in a production setting, built world class data pipelines using cutting edge tools (e.g. Spark).
• 10+ years of hands-on experience shipping models to production, working on a variety of problem spaces (eg: user modeling, spam classification, prediction, clustering etc)
• MS in Computer Science, Statistics, Math, Science (physical or social), Engineering or similar quantitative and computation field plus 5+ years of industry experience
• Strong software engineering and coding skills with the ability to write production quality code
• Strong understanding of probability & statistics, machine learning, and algorithms
• Experience with SQL and NoSQL databases
• Experience working with Linux, shell scripting
• Previous startup experience strongly preferred

Bonus Points

• Experience implementing applications on Google Cloud Platform
• Experience with deep generative models or graph learning a plus.
• Tag your application with your solution to an active / recently concluded Kaggle competition, to get ahead of the list :)
• Experience leading a team of data and data scientists; Strong ability to multiply the effect of the team and the team members; excellent written communication; self-starter that can balance sophistication with practicality 

Why Narvar?

We're on a mission to simplify the everyday lives of consumers. Post-purchase is a critical phase of the customer journey. That's why we created Narvar - a platform focused on driving customer loyalty through seamless post-purchase experiences that allow retailers to retain, engage, and delight customers. If you've ever bought something online, there's a good chance you've used our platform!

From the hottest new direct-to-consumer companies to retail’s most renowned brands, Narvar works with GameStop, Neiman Marcus, Sonos, Nike, and 1300+ + other brands. With hubs in San Francisco, Atlanta, London, and Bangalore, we've served over 125 million consumers worldwide across 10+ billion interactions, 38 countries, and 55 languages.

Pioneering the post-purchase movement means navigating into the unknown. Our team thrives on this sense of adventure while nurturing a mindset of innovation. We're a home for big hearts and we leave our egos at the door. We work hard but we always make time to celebrate professional wins, baby showers, birthday parties, and everything in between.

We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

#LI-BB1

#LI-Remote

What we’re looking for

The Machine Learning Platform team (MLP) is seeking a Senior Software Engineer II to design and implement the secure, highly scalable, and high-performance pipelines that govern the end-to-end lifecycle of ML models.

You will work at the intersection of Data Science and DevOps, building the "connective tissue" that empowers our product portfolio to leverage technologies like Generative AI, Natural Language Processing (NLP), and real-time classification. We are looking for a subject matter expert who will drive innovation and deliver results that meet the dynamic needs of a high-growth AI environment. You will report to the Senior Engineering Manager on the MLP Solutions team.

What you’ll be working on

• Build and maintain robust ML systems using Python (Pandas/NumPy, PyTorch/transformers) to support efficient ML operations.
• Design ML cloud infrastructure using AWS services, ensuring the platform is built for reuse, scalability, and high-volume throughput.
• Collaborate with application engineers to integrate, test, and monitor ML model services across SurveyMonkey’s microservices architecture.
• Act as a consultant to internal feature teams, educating and influencing decisions regarding ML tooling and infrastructure.
• Incorporate telemetry into the platform for complex failure mode analysis, ensuring real-time reliability for millions of daily survey responses.
• Serve as a technical leader and mentor, championing high standards in coding, documentation, and architectural best practices to foster the professional growth of junior engineers on the MLP team.

We’d love to hear from people with

• 8+ years of professional ML development experience with a proven track record of designing and implementing ML infrastructure in AWS.
• Mastery of ML concepts (supervised/unsupervised learning) and deep understanding of Large Language Models (LLMs) and NLP.
• Expert-level Python skills and experience with high-scale data processing (PySpark, etc.).
• Comfortable with Unix/Linux systems and experienced in deploying end-to-end solutions for real-time applications (spam detection, personalization, ranking).
• Bachelor’s Degree in Computer Science, Data Science, Software Engineering, Mathematics, Statistics, or a related quantitative field.

The base pay provided for this position ranges from CAD $126,400.00/ year - $158,000.00/ year depending on the geographic market and assuming a full-time schedule. Actual base pay is based on a number of factors including market location, job-related knowledge, education or training, skills, and experience.

Bonuses may also be offered as part of the total compensation package, in addition to a competitive benefits package including medical, dental, vision, life, and disability insurance; RRSP matching; flexible spending & health savings account; paid holidays; paid time off; employee assistance program; and other company benefits. 

This opening is for an existing vacancy.

SurveyMonkey believes in-person collaboration is valuable for building relationships, fostering community, and enhancing our speed and execution in problem-solving and decision-making. As such, you will be required to work from our Ottawa office up to 1 day per week.

#LI-Hybrid

Position Overview:

Come join the team at Zynga making an impact across all of the company’s games - Mobile Game Tech! Do you love developing data models and visualizations to help teams make strategic decisions? This analyst role is a key figure in our cost transparency efforts on the FinOps team, delivering critical insights into service costs. This position also provides dashboards, data visualizations, and analyses for areas that make our org effective at serving our internal customers, allowing all teams to make better decisions.

What You'll Do:

• Lead financial analysis activities with regards to internal cost allocations for technology spend
• Lead data model design and data analysis aspects of the FinOps team’s software development, including close collaboration with other FinOps software engineers
• Engage with customers and stakeholders to design and iterate upon user-facing data visualizations
• Develop data ingestion and normalization to serve data visualizations and dashboards, as well as owning maintenance and ensuring ongoing data quality
• Support FinOps guidelines and work with other teams and partners to implement cost analysis and optimizations for technology spend across Zynga
• Interact with members of Finance and the C-suite with regards to technology cost allocations, forecasts, and ad hoc FinOps-related requests

What You Bring:

• B.S. in Computer Science, Math, Statistics, Engineering, or other quantitative field (or equivalent experience)
• Demonstrated proficiency with Python for data cleaning and data wrangling (pandas, NumPy), and ability to work with source code via pull requests, CICD pipelines, etc
• 8+ years of hands-on experience with the following: SQL, data warehouses, Tableau or similar data visualization tools
• Ability to design monitoring rules for validating data quality within data pipelines
• Experience collaborating with customers to assess their analysis and visualization needs and design solutions that present the data effectively and clearly
• Ability to think through the UI/UX of data visualizations to best serve the target audience's needs
• Comfort interacting with stakeholders at a much more senior level, ability to adjust communication style to the audience
• Experience working autonomously and capable of tackling solo projects with ease

Bonus Points:

• Familiarity with FinOps practices
• Familiarity with financial concepts related to budgeting, forecasts, amortization, etc
• Previous experience with software engineering platforms such as AWS, Splunk, Datadog

What We Offer You:

Zynga offers a world-class benefits package that helps support and balance the needs of our teams. To find out more about our benefits, visit the Zynga Benefits site.

The pay range for this position in Ontario at the start of employment is expected to be between $119,140 and $169,140 per year. However, base pay offered is based on market location, and may vary further depending on individualized factors for job candidates, such as job-related knowledge, skills, experience, and other objective business considerations.

Subject to those same considerations, the total compensation package for this position may also include other elements, including a bonus and/or equity awards and eligibility to participate in our 401(K) plan and Employee Stock Purchase Program. Regular, full-time employees are also eligible for a range of benefits at the Company, including: medical, dental, vision, and basic life insurance coverage; 14 paid holidays per calendar year; paid vacation time per calendar year (ranging from 15 to 25 days) or eligibility to participate in the Company’s discretionary time off program; up to 10 paid sick days per calendar year; paid parental and compassionate leave; wellbeing programs for mental health and other wellness support; family planning support through Maven; commuter benefits; and reimbursements for fitness-related expenses. The use of Artificial Intelligence is not being used to screen candidates. The position is for an existing vacancy.

About the role:

Responsibilities:

• Design, develop, and deploy Cresta’s AI Agent solutions and proprietary models.
• Focus on practical AI challenges such as improving reasoning, planning capabilities, and evaluation in real-world scenarios.
• Collaborate with cross-functional teams including front-end and back-end software engineers to integrate AI Agents into Cresta’s customer solutions.
• Lead initiatives to scale AI systems for production environments, ensuring performance and reliability across use cases.
• Contribute to solving cutting-edge problems in AI and help define the future roadmap for Cresta’s AI Agents.
• Innovate and research ways to improve security, cost-efficiency, and reliability of AI systems.

Qualifications We Value:

The role

Are you passionate about food? Do you consider yourself an expert and enthusiast in developing and deploying large-scale recommender systems? Then this position might be the perfect fit for you!

You will join a high-impact team of data scientists, focusing exclusively on menu personalization. As a Senior Data Scientist, you will play a pivotal role in designing, implementing, and improving our personalized recommendation algorithms for culinary products across HelloFresh’s global markets. In this role, you can expect to have a large and tangible impact on customer experience and business growth by ensuring customers always see the most relevant and appealing menu options. You will work closely with other personalization Data Scientists, as well as Data, Backend, and Machine Learning Engineers, together delivering statistical solutions and ML models to production.

To succeed in this role you should be curious, able to deal with uncertainty, a fast learner, and someone who relentlessly prioritizes problem statements and tailors the solution accordingly. You should also be comfortable suggesting multiple solutions based on your experience, and prioritizing tasks based on effort and potential impact. As a senior member of the team, you will be expected to work independently when necessary, as well as support other team members occasionally. 

What you’ll do

• Join and become an integral part of a high-impact team covering menu personalization.
• Contribute to designing and implementing the next generation of our recommender models, from experimentation and data collection to operationalization and closely aligning them with non-technical stakeholders.
• Shape and transform the future of customer experience at HelloFresh by proposing new personalization approaches and evolving the statistical and ML models in pace with future company strategies.
• Design and execute A/B tests to measure the impact of new recommendation strategies on key metrics.
• Work with state-of-the-art technologies like AWS (EMR, Glue, S3, etc), Kafka, PySpark, Airflow, Databricks, MLFlow, Claude (AI), as well as our in-house MLOPs tools.
• Collaborate closely within other functions (data engineering, backend engineering, ML engineering, product) to translate business objectives into concrete, data-driven personalization strategies.
• Analyze the performance of our models to understand customer behavior, and come up with improvements.
• Contribute to a collaborative and knowledge-sharing environment.

What you’ll bring

• Proven track record of scientific work in your Bachelor's, Master's, or PhD in computer science, statistics, physics, economics, mathematics, data science or similar.
• Long-term (3+ yrs) work experience as a (senior) data scientist focused on developing and deploying recommender systems or personalized ML models in a production environment.
• In-depth knowledge of various recommendation algorithms, evaluation metrics, and the practical challenges of building and maintaining a modern recommender system.
• Fluency and long-term experience in Python, including high familiarity with its scientific stack such as Numpy, Pandas, Scikit-learn, Matplotlib. (Experience in Databricks and PySpark is a plus).
• Ability to retrieve (using SQL), manipulate, and analyse large, heterogeneous datasets.
• Experience with software development practices and tools (Git, CI/CD, Docker, AWS).
• You love to learn, are a critical thinker and creative problem solver who strives for excellence.
• You are a team player who loves to collaborate and work with stakeholders from different backgrounds in an international environment.
• Exceptional communication and collaboration capabilities.
• Experience working with and leveraging Generative AI tools (e.g., Claude, Copilot) to support model development, experimentation, code generation, and documentation.

Interacting with product managers, UX designers, data and backend engineers, as well as business stakeholders, is very much part of our day-to-day, so communication skills are vital. We are looking for strong problem-solvers who can apply their statistical modeling and machine learning skills to a wide range of business problems in the personalization space while also acting as an ambassador to coach team members and stakeholders.

Above all, we are looking for people who will make HelloFresh better. We believe there are many different ways of developing skills, and we love diverse experiences! So even if you don’t “tick all the boxes” but think you’d thrive in this role, we would really like to learn more about you.

Let’s cut to the cheese, this is why you'll love it here

• Box Discount - Amazing discounts on 1 box per week! 75% discount on weekly HelloFresh and Chefs Plate meal kits AND 50% off weekly Factor meal box.
• Health & Wellness - Health & Dental benefits from day 1, a Health Spending Account, unlimited access to the Headspace app to meet your self-care needs, and 25% discount on GoodLife fitness memberships!
• Vacation & PTO - Time off is also an important part of self-care! We offer generous vacation and PTO to help you create a good work-life balance. 
• Family Benefits - A parental leave top-up program for expectant parents.
• Growth & Development - We support your career progression and invest in your continued learning through experiences and initiatives owned by our dedicated L&D team
• Work Hard & Have Fun - From team socials to engaging company days, you’ll have plenty of opportunity to experience the fun!
• Diversity & Inclusion Initiatives - With impactful ERG’s like FreshPride, Women Empowered and LIMES, we are committed to our diversity, equity & inclusion efforts.
• Food Puns - this one is kind of a big dill if you haven’t already noticed. We even have some punny meeting room names!

Flexible Hybrid Approach

At HelloFresh, we know that flexible work arrangements are essential in enabling you to do your best work, while balancing your personal and life needs. Offering remote work flexibility, along with the opportunity to interact and collaborate in the office are all a part of creating a great employee experience. 

To meet these needs, we are pleased to provide Flexible Hybrid work. Flexible Hybrid is a people-first approach that is based on choice, trust, personalization, and empowers teams to choose when and how often they work from the office and work from home, in addition to team days and company days. This means a minimum of 2 days in office per week, with most teams in office between 2-3 days a week.

#LI-HYBRID

HelloFresh Canada uses AI-integrated technology to help us process and evaluate applications more efficiently. This includes tools that screen and assess candidate qualifications based on the requirements for this role. While these tools assist our workflow, all final selection decisions are made by our hiring team.

This is a posting for an existing vacancy. We are actively seeking to fill this position.

#DATA

The ML Platform Engineering team at Afresh is responsible for building and maintaining the foundational infrastructure and tooling that powers all of our machine learning and applied science solutions. We provide the shared components and services that enable our teams to develop, deploy, and scale robust ML models. This includes a performant data API, configurable featurization, reliable forecasting systems, highly parallel optimization engines, and scalable training pipelines, and deep experimentation capabilities. As our product suite and customer base grow, so does the scale and complexity of what our platform needs to support, gracefully accommodating predictions and simulations across various time scales (hours, days, weeks), complex data hierarchies (pallets on a truck, shelves of mangos in a store, chunks of fruit in a bowl), and endless configuration possibilities (average shelf fullness, backroom loads, truck capacities).

About the Role

As an ML Platform Engineer on the ML Platform Engineering team, you will be instrumental in elevating our core ML platform to its next level of performance, reliability, and scalability. You'll work on the critical infrastructure that directly enables all of Afresh's Machine Learning and Applied Science teams to innovate faster and deliver impact. Your contributions will empower our product suite, including our flagship Prediction Engine, to power replenishment decisions on more than 15% of all produce sold in the United States.

What You'll Do

• In your first 3 months, you might deliver a feature that helps generalize model configuration, enables no-code model deploys for our various ML solutions, or vastly improves integration testing across our ML systems.
• By the end of your first 6 months, you will have owned the implementation of significant scalability improvements and additions to our ML platform. This might include new feature pipelines that power our recommendation engine, or work to stand up the first instance of real-time inference at Afresh.

Skills and Experience

• BS in Computer Science or a relevant technical field.
• 3+ years of professional software development experience with a proven track record of shipping high-quality applications and services.
• Experience working collaboratively with machine learning engineers, data scientists, or applied scientists on large-scale software projects involving machine learning models.
• Deep expertise in library design, API design, data structures, and algorithms.
• Strong familiarity with Python.

Tech Stack: Our backend is pure Python (NumPy, Pandas, Torch, PySpark, Cython, orchestrated in Airflow). We use Databricks as our data warehouse. While we'd like you to have very good familiarity with Python, many of our problems are stack-agnostic.

This position is not eligible for company sponsorship.

Salary Band in Canada: $114,00 - 154,000

Salary Band in U.S.: $130,000 - $176,000

Junior Data Scientist

The Wiley Edge banking team is currently seeking a Data Scientist to work across the Front Office Trading, Risk & Regulatory Compliance Trade Analytics group, using Machine Learning and Data Analytics techniques to design and develop complex data models for client and trading analytics across their Fixed Income and FX offerings.

You will be supporting the transformation from traditional controls monitoring to “on-screen” data driven monitoring and analytical solutions.

Tools:

• Python (pandas, numpy)
• Machine Learning Algorithms / Natural Language Processing (NLP)
• AI / Robotics
• Predictive Analysis
• Good grasp of ML Algorithms

Starting Salary: CA$85,000 per annum

Junior Data Scientist