Senior Data Scientist

Quick Facts

Senior Data Scientist focused on turning data into actionable, business-impacting insights.

Description

You will identify business problems, select the right data and methods, and build analytical and predictive models that improve decisions. The role includes working with large volumes of structured and unstructured customer data, creating data visualizations, and developing data pipelines for feature engineering and data quality tasks. You will present findings clearly, collaborate with stakeholders, and mentor other data scientists.

Responsibilities

• Identify business problems, find the right data and methods to address them
• Process large volumes of structured and unstructured customer data using advanced analytical solutions
• Turn analyzed data into unique actionable insights and business value
• Create high-quality data visualizations
• Create data pipelines for feature engineering, missing values treatment, and outliers detection
• Research, build, and validate analytical, mathematical, econometric, descriptive, and predictive models
• Estimate, plan, and coordinate delivery of moderate/large sized projects
• Evaluate and select the best matching data science tools and libraries
• Present findings clearly and informatively
• Communicate effectively with stakeholders, product managers, and engineers
• Continuously develop the team’s knowledge base and participate in brainstorming
• Educate junior and mid-level data scientists

Requirements

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

Benefits

• Career development programs and mentoring
• Thought leadership, soft skills and well-being programs
• Certifications (Anthropic, Gemini, GCP, Azure, AWS) and English classes
• Stable pay and Employee Stock Purchase Plan with a 15% discount
• Health insurance, multisport, and shopping vouchers
• Referral bonuses up to $2,000
• Hybrid work with the option to work remotely within Poland
• Chance to work abroad up to 60 days annually and business-driven relocation opportunities
• Office amenities and corporate, social, and well-being events