Senior Data Scientist

We're currently looking for specialists for the position of Senior Data Scientist.

If you're interested, please take a look at the job offer below.

What can we offer?

• B2B Contract
• Work mode: Fully remote in Poland
• Long-term project for a multinational technology company

Requirements:

• 10+ years of industry experience solving analytical problems using quantitative approaches, including defining metrics and goals, monitoring key metrics, understanding root causes of changes in metrics, and exploratory analysis to discover new opportunities.
• 10+ years of industry experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), and/or statistical/mathematical software (e.g., R).
• Strong background in data analysis, statistical modeling, and data visualization.
• Excellent communication skills, with the ability to present complex data insights to both technical and non-technical audiences.
• Very good verbal and written English skills (at least at B2 level)

Responsibilities:

• Data Analysis: Perform exploratory data analysis (EDA) to identify trends, patterns, and correlations; apply statistical and quantitative tools or techniques; interpret data and formulate deep-level insights.
• Setting Goals and Success Metrics: Collaborate with stakeholders to define business objectives and key results (OKRs); define success metrics that measure short and long-term progress towards OKRs.
• Visualization: Design visualizations that provide a clear narrative understanding as well as deep dive capabilities; develop high-performance reports and dashboards.
• Storytelling Through Data: Develop compelling narratives to communicate data insights for technical and non-technical audiences; utilize various relevant tools and techniques to effectively present data; provide recommendations based on analytical findings and insights, with prioritization of actions and estimated impact.
• Modeling: Develop and implement predictive and forecasting models using multiple techniques such as regression, clustering, classification, and more; evaluate and refine models based on performance metrics; generate actionable insights from model output.
• Research and Experiment: Formulate multiple hypotheses that are tied together to answer business questions; develop a comprehensive research plan that identifies appropriate data collection tools, techniques, or methods to be used for a specific research problem; conduct research with appropriate methods to answer the research questions or test hypotheses.