Lead Data Scientist

ABOUT THE COMPANY

Our client is an end-to-end data services partner to global enterprises, founded in 2008 and headquartered in Warsaw. Our teams work with over 75 leading consumer packaged goods brands across more than 30 countries, helping them unlock the full value of their data — from strategy and development through to operations and adoption.

Our work spans supply chain analytics, customer analytics, AI and machine learning, data platforms, and digital commerce. We are recognised as a Strong Performer in the Gartner Peer Insights Voice of the Customer report for data and analytics, and hold Great Place to Work certification in multiple countries.

ABOUT THE ROLE

Our Data Science and AI team delivers machine learning solutions for global clients, with a particular focus on forecasting, customer analytics, and causality frameworks. Projects span next best offer and action modelling, propensity and churn modelling, demand and sales forecasting, and revenue growth management.

As Lead Data Scientist, you will take ownership of delivery across end-to-end classification and forecasting use cases — setting technical direction, driving quality, and ensuring the team produces work that connects directly to business outcomes. You will be the senior technical voice in client engagements, leading requirements gathering, shaping solution approaches, and supporting pre-sales activity. Alongside delivery, you will mentor other data scientists and be expected to raise the standard of the practice around you.

WHAT YOU'LL WORK ON

Technical leadership across use cases

Own end-to-end delivery of classification and forecasting projects — from problem framing and data preparation through model development, evaluation, and deployment support. Set the technical direction and hold the bar on quality.

Data exploration and quality

Lead exploratory data analysis on tabular and time-series data, identify quality issues, and define feature engineering approaches that feed production models.

Model development and validation

Train, tune, and validate ML models — logistic regression, tree-based models, gradient boosting, simple neural networks, and classical time-series models — with clear evaluation frameworks tied to business KPIs.

Stakeholder engagement

Lead business requirements gathering, define success metrics, assess data feasibility, and manage stakeholder expectations at senior level. Build clear visualisations and concise reports to communicate model results.

Production collaboration

Work with data engineers and AI engineers to bring models into production — batch scoring, APIs, model monitoring, and dashboards — ensuring handoffs are clean and well-documented.

Documentation and reproducibility

Set standards for documenting data sources, modelling assumptions, and experiment results across notebooks, reports, and wikis.

Mentorship and pre-sales

Mentor other data scientists, raise practice standards, and contribute to pre-sales activities including scoping, estimation, and solution design for prospective clients.

WHAT WE LOOK FOR

Strong classical data science and ML background

Extensive commercial experience across the full ML workflow, with a track record of delivering production systems that drive measurable business impact.

Customer analytics or advanced forecasting expertise

Deep hands-on experience in customer analytics (propensity, churn, next best action) or advanced forecasting (demand, sales). Familiarity with causality frameworks is a strong advantage.

Hyperparameter tuning and validation frameworks

Expert knowledge of model tuning approaches and validation frameworks, with a clear understanding of how metric choices connect to business outcomes.

Business requirements and technical planning

Proven ability to lead requirements gathering with non-technical stakeholders, define success metrics, and translate ambiguous business problems into well-scoped technical plans.

Python and SQL

Fluent in Python for data science and modelling work. Solid working knowledge of SQL for data access, exploration, and pipeline work.

THE TEAM

You'll join a specialist Data Science and AI practice working alongside experienced consultants, ML engineers, and data engineers. The team delivers solutions for large international clients across CPG, retail, and manufacturing. There is a strong knowledge-sharing culture, with internal communities, competency centres, and structured learning programmes built into how the team operates.

COMPENSATION & BENEFITS

Rate

140 – 150 PLN per hour on a B2B contract, depending on experience.

Contract flexibility

Flexibility on working hours and preferred form of contract.

Workation policy

Option to work remotely from other locations for defined periods.

Onboarding

Comprehensive online onboarding programme with a dedicated buddy from day one.

Learning and development

Unlimited access to the Udemy learning platform from day one. Certificate training programmes, upskilling support, capability development programmes, competency centres, knowledge sharing sessions, community webinars, and over 110 training opportunities per year.

Career growth

Internal promotion pathways — 76% of managers were promoted internally. Cooperation with top-tier engineers and domain experts across the organisation.

Referral bonuses

Financial rewards for successful employee referrals.

Wellbeing

Activities to support health and wellbeing, with opportunities to contribute to charitable causes and environmental initiatives.

Employer recognition

Great Place to Work certified employer.