Consultant Data Scientist - Classical Data Science & ML Models

Job Title Deliver business solutions with ML and Data Science, focusing on Forecasting and Customer Analytics, collaborating with stakeholders to translate business problems into ML solutions. 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 a consultant in this team, you will work end-to-end on classification and forecasting use cases — from problem framing and data preparation through to model development, evaluation, and deployment support. You will collaborate closely with business stakeholders and data engineers, and be expected to translate business problems into well-defined machine learning goals. At senior consultant level, the role also involves pre-sales activity. What You'll Work On End-to-end modelling: Own classification and forecasting use cases from problem framing through data preparation, feature engineering, model training, and evaluation — covering demand forecasting, churn prediction, and similar applications. Data exploration and quality: Perform exploratory data analysis on tabular and time-series data, identify quality issues, and engineer features that feed production models. Model development and validation: Train, tune, and validate standard ML models — logistic regression, tree-based models, gradient boosting, simple neural networks, and classical time-series models — using appropriate evaluation metrics tied to business KPIs. Stakeholder communication: Build clear visualisations and concise reports to present model results and insights to business stakeholders. Translate complex systems into plain language. Production collaboration: Work with data engineers and AI engineers to bring models into production — batch scoring, APIs, model monitoring, and dashboards. Documentation: Document data sources, modelling assumptions, and experiment results in a reproducible way across notebooks, reports, and wikis. What We Look For Classical data science and ML experience: Solid commercial experience with the full ML workflow — from EDA and feature engineering to model training, validation, and deployment handoff. Customer analytics or forecasting background: Hands-on experience in customer analytics (propensity, churn, next best action) or advanced forecasting (demand, sales). Familiarity with causality frameworks is a plus. Hyperparameter tuning and validation: Practical 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: Experience gathering requirements from non-technical stakeholders, defining success metrics, assessing data feasibility, and aligning expectations across teams. Python and SQL: Fluent in Python for data science and modelling work. Basic working knowledge of SQL for data access and exploration. 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, primarily in CPG, retail, and manufacturing. There is a strong knowledge-sharing culture, with internal communities, competency centres, and regular learning programmes built into how the team operates. Compensation & Benefits Rate: 105 – 140 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 and 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, opportunities to contribute to charitable causes and environmental initiatives. Equipment: Modern office equipment provided. Employer recognition: Great Place to Work certified employer.