AutoScout24 is on a mission to revolutionize how marketing decisions are driven through data and analytics. We are seeking an experienced and motivated Marketing Data Analyst (m/f/d) to take ownership of our marketing analytics framework and help us make data-informed decisions to optimize efficiency and ROI of our multi-channel marketing funnel. This role is perfect for someone who combines strong technical skills with sharp business judgment - and who thrives working close to top management on high-impact, complex analyses. 

Our team treats AI as a core working principle. We actively integrate AI tools into our day-to-day workflows to raise the speed, quality, and depth of our analytics work — and we expect this mindset from everyone who joins us. 

What you will do 

• Design and implement scalable data models and pipelines within our Data Lake, to integrate and manage marketing data across channels. 

• Build and maintain engaging dashboards to monitor demand, user acquisition and marketing efficiency, following best practices in data visualization to deliver clear and actionable insights. 

• Empower marketing teams with self-service analytics by curating reliable data sources and building automated, scalable data solutions that enable independent, data-driven decision-making. 

• Work closely with online, app, and offline marketing teams to deliver insights and recommendations that optimize multi-channel efforts, media planning, budget allocation, and user acquisition. 

• Partner with fellow analysts to evaluate performance across key platforms — including Google Analytics, Adjust, AppsFlyer, Google Ads, Meta, and Criteo — and translate findings into strategy. 

• Lead exploratory analytics projects — including new KPI development, A/B testing, incrementality studies, offline measurement, and attribution modelling — to address evolving business challenges. 

• Contribute technical expertise and business judgment to deep-dive analyses and reports for senior and top management, and lead multi-disciplinary projects end-to-end, managing timelines and stakeholder communication. 

• Leverage AI tools and automation to increase the efficiency, quality, and scale of analytical work — from data preparation and insight generation to reporting and stakeholder communication. 

What you will bring 

• 4+ years of experience in marketing analytics, ideally in online marketplaces, B2C or B2B environments, with a strong grasp of online and app marketing, campaign optimization, and budget allocation. 

• Proficiency in SQL and hands-on experience building data pipelines and ELT workflows using tools like Airflow and dbt, with familiarity across cloud platforms such as BigQuery, Redshift, or Snowflake. 

• Strong experience with BI and dashboarding tools (Tableau, QuickSight, PowerBI, or similar) and a solid understanding of data visualization best practices. 

• Deep understanding of Google Analytics, MMPs (Adjust, AppsFlyer), and major ad platforms (Google, Meta, Criteo), including tracking systems and reporting structures. 

• Proven ability to translate complex data into clear, actionable recommendations for both technical and non-technical audiences, including C-level stakeholders. 

• Outstanding attention to detail and a strong analytical mindset, with a grasp of statistical concepts to deliver effective and pragmatic solutions. 

• A proven track record of proactively and independently leading cross-functional analytics initiatives in a collaborative, multi-level organization. 

• A proactive approach to integrating AI tools into analytical workflows — with genuine curiosity about how AI can improve the speed and quality of data work. 

• Excellent verbal and written English skills. 

Nice to have 

Experience with web/app marketing attribution, budget planning, or forecasting models · Amazon QuickSight · Google Search Console · Google Tag Manager  

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs. The ML Workflows Engineering team is dedicated to removing infrastructure challenges, streamlining machine learning operations (MLOps), and enabling teams to focus on the innovative work that matters most – building impactful ML models and intelligent agents. As part of the team, you'll play a key role in designing tools, automation, and pipelines that make machine learning development seamless and intuitive.

By integrating cutting-edge MLOps practices and engineering excellence, we aim to maximize productivity and remove the complexity of ML infrastructure so that our teams can push the boundaries of what’s possible in AI.

As part of our team, you will:

• Build tools, automation, and workflows to simplify infrastructure-heavy tasks, empowering AI teams to focus on experimentation and solving core challenges.
• Develop robust monitoring, logging, and tracing systems to ensure the performance and reproducibility of ML workflows in production.
• Design, implement, and maintain end-to-end machine learning pipelines to enable the seamless development, training, and deployment of ML models and intelligent agents.
• Work with large-scale distributed systems, including GPU clusters, to support training, fine-tuning, and evaluation of ML models.
• Collaborate with product and development teams to transform high-level goals into concrete, scalable, and maintainable systems.
• Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive and focused on innovation.

We’ll be happy to have you on our team if you have:

• Hands-on experience with modern MLOps tooling, including Kubernetes, Cloud providers (GCP and AWS), and ML orchestration frameworks.
• A solid understanding of the ML lifecycle from idea to the customer-facing application.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A customer-centric mindset – you care about how ML engineers are actually working and can translate their needs into actionable, scalable, and maintainable architectural decisions.
• Experience with modern CI/CD systems, like GitHub Actions or JetBrains TeamCity.
• At least three years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as ZenML, Dagster, and Airflow.
• Developing infrastructure components and services using cluster solutions like Kubernetes.
• The development of Python-based backend services.
• Creating and maintaining ML pipelines, including legacy ones.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.

We’d be especially thrilled if you have experience with: 

• LLM inference frameworks such as vLLM, DeepSpeed, and TensorRT.
• Writing and maintaining Python libraries used by internal (or external) ML engineers.
• A strong theoretical background in NLP and transformer-based approaches.
• Writing code in Java and/or Kotlin.

#LI-HYBRID
#LI-MR1

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.

We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.

You’ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into JetBrains products.

As part of our team, you will:

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
• Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
• Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
• Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.
• Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.
• Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.
• Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features. 

We’ll be happy to bring you on board if you have:

• Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
• Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
• Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.
• At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.
• Large-scale data and training pipelines, e.g. MapReduce-style clusters, multi-node GPU training, or workloads on the order of 1M+ CPU/GPU hours.
• Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.
• AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.
• Inference optimization and serving optimized models in production.

#LI-KP1