Here’s a summary of the role:   

You’ll use your applied machine learning and generative AI skills to design and ship LLM powered product experiences that solve real problems for real users. You turn messy documents and complex language into smart assistants, agentic solutions, and search experiences that feel fast, reliable, and trustworthy. 

In this role you work end-to-end: framing problems, shaping prompts and workflows, orchestrating tools and retrieval, and partnering with engineers and product managers to bring modern AI into production. If you enjoy building with large language models and care deeply about evaluation, safety, and impact, you’ll feel at home here. 

Here’s a breakdown of what you’ll do (not all of it, just the important stuff): 

• Design, prototype, and refine LLM based features over large document sets. 

• Orchestrate multistep, agentic workflows and tool calling (including MCP servers and other integrations) so LLMs can take meaningful actions, not just generate text. 

• Define evaluation frameworks, experiments, and metrics so you can measure model quality, relevance, and safety with confidence. 

• Use AWS services (i.e. AWS Bedrock) to deliver reliable, secure, and scalable AI capabilities. 

• Work closely with domain experts, PMs and software engineers to turn prototypes into reliable, production ready services. 

• Handle complex, domain specific text (e.g. legal, financial, or regulatory content) with a strong focus on accuracy, robustness, and privacy. 

• Communicate your findings clearly, share best practices, and stay up to date with the latest ML and AI research that can improve your solutions. 

These are the essentials you’ll need to get an interview: 

• Hands-on experience building LLM powered applications or features (for example RAG systems, chatbots, assistants, agentic solutions or document centric workflows) — not just using chat interfaces. 

• Strong Python skills and everyday comfort with the modern data / AI ecosystem (e.g. pandas, NumPy, basic ML libraries, HTTP APIs, and SDKs for cloud AI services). 

• Practical experience with LLM powered solution design, evaluation, and iteration: you know how to test, compare approaches, and choose the one that works best with real data. 

• Familiarity with at least one cloud platform (ideally AWS) and building or contributing to services or pipelines that run in production. 

• A degree in Computer Science, Data/Applied Science, Mathematics, Statistics, or a related technical field, or equivalent practical experience. 
  
• Clear communication skills, collaborative mindset, and a genuine interest in learning, sharing knowledge, and iterating quickly based on feedback. 

It would be great if you had these to, but we’ll support you if you don’t: 

• Experience designing and implementing agentic workflows (multistep reasoning, tool calling, planners) and/or building MCP servers or similar tool integrations. 

• Hands-on experience with AWS Bedrock or comparable platforms (model selection, configuration, guardrails, evaluation, and cost/performance trade-offs). 

• Experience with semantic search, vector databases, and information retrieval techniques in production systems. 

• Familiarity with observability and evaluation for LLM applications (e.g. logging, feedback loops, offline and online evaluation, safety checks). 

• Experience working with complex or regulated content such as legal, risk, compliance, financial, or governance documents. 

#LI-Hybrid

We are currently looking for a well-versed (Senior) Data Scientist to join the team to deliver consulting expertise our EMEA and US-based customers.

What (exactly) will you do?

As a (Senior) Data Scientist, your responsibilities will span across the following dimensions:

Project Planning & Delivery

In the planning phase of a project, you're expected to lead or participate in scoping the engagement, designing high-level architecture, defining subtasks, and project delivery schedules. After the planning phase is over, you'll deliver the project on your own or within a mixed DS/DE team setup where you'll oftentimes take the role of a technical lead. Furthermore, during the project delivery, you will be client-facing, acting as DATAPAO's interface or SPOC to our customers.

During non-delivery cycles, you will provide ML/DS consultancy to technical teams for our EMEA customers. Our customers are either direct DATAPAO customers or coming via our Databricks partnership.

Data Science & AI consultancy

As a consultant, you will work closely with Databricks and cloud providers to understand the needs and requirements of clients. Based on your understanding, you will recommend solutions that leverage Databricks or native cloud features. Additionally, you will be responsible for conducting workshops to enable clients on these platforms.

Internal Solution Development

Besides customer projects, you’ll also work on internal solution development and research initiatives. Furthermore, you will be expected to grow your industry-specific knowledge and learn new technologies & methodologies by participating in the Data Science Group and by delivering solutions for internal usage.

Wider Organizational Initiatives

Occasionally, you can participate in developing Data Science and Machine Learning courseware (for our Education arm) to be delivered to customers or internal teams. You could also add value by providing technical (pre-sales) support for our Sales Team or creating content for marketing and sales.

Last but not least, if you would like to and have the required people skills, you can mentor other members of the data science guild. This includes regular tech mentorship meetings, one-on-ones, knowledge sharing sessions, and participation in performance reviews among others.

What does it take to fit the bill?

Soft Skills

Tech Skills

• Solid software engineering foundations (git, clean code principles, OO programming, etc.);

What do we offer?

• Flexible PTO and Working Hours - we’re flexible about your working routine and how many days you take off if it does not conflict with customer success, company goals, and getting the work done.

• Flexible Office - it is up to you which days you spend in the office. However, as a strong community is one of DATAPAO's value propositions, we’d expect you to spend one day per week in the office on average. 

• 30 Calendar Days of Paternity Leave (if applicable) - we think it’s essential for both parents to be there when it matters the most. 

• Private Medical Insurance - we provide a private medical package via Generali. Employee Assistance Program (EAP): Through EAP, we offer our employees access to psychological, financial, and legal consulting services via phone or in person.