Senior Data Scientist

Quick Facts

• Senior Data Scientist responsible for turning data into actionable knowledge.
• Focus: solving business problems in the technological domain of Data Science.

Description

Senior Data Scientist role focused on turning structured and unstructured customer data into actionable insights and business value. You will build data pipelines, create visualizations, and research, build, and validate analytical and predictive models. The role includes presenting findings and communicating complex results to stakeholders across departments.

Responsibilities

• Identify business problems, find relevant data and methods
• Process large volumes of structured and unstructured customer data with advanced analytical solutions
• Produce actionable insights and measurable business value
• Create high-quality data visualizations
• Build data pipelines for feature engineering, missing value handling, and outlier detection
• Research, build, and validate analytical, mathematical, econometric, descriptive, and predictive models to improve decisions
• Estimate, plan, and coordinate delivery of moderate to large projects
• Evaluate and select the best Data Science tools and libraries
• Present findings clearly and informatively
• Communicate with stakeholders, product managers, and engineers about complex technical and data-driven topics
• Continuously develop the team’s knowledge base and participate in brainstorming
• Educate junior and middle Data Scientists

Requirements

• 3+ years of relevant experience working with a variety of information systems and data
• Writing high-quality Python code according to PEP-8 guidelines
• Strong knowledge of data exploration and analysis techniques; ability to build models/analytical frameworks, develop hypotheses, and design experiments
• Advanced knowledge in at least one domain: Computer Vision, Time Series, Natural Language Processing, Recommendation Systems, Predictive Analytics
• Knowledge of EDA, classical vs advanced ML models, performance metrics/losses, and model fine-tuning
• Knowledge of pros and cons for SOTA ML architectures and applicable use cases
• Understanding of ML projects’ life cycle and ability to follow methodology processes (CRISP-DM, TDSP, LeanDS, etc.)
• Ability to design and supervise data labeling processes
• Ability to interpret/report results and provide business recommendations; ability to coach and train others

Benefits

• Hybrid work model with opportunity to work remotely within Poland
• Chance to work abroad for up to 60 days annually and business-driven relocation opportunities
• Career development programs; mentoring and soft-skill/well-being programs
• Certification options (Anthropic, Gemini, GCP, Azure, AWS)
• English classes
• Stable pay; Employee Stock Purchase Plan with a 15% discount
• Benefits package (health insurance, Multisport, shopping vouchers)
• Referral bonuses up to $2,000
• Office amenities and social/well-being events
• Benefits are available to employees only; B2B/contractor options may be available with individually agreed terms