Senior Data Scientist

Responsibilities:

• Develop predictive models that generate insights, recommendations, and business signals.
• Perform data discovery, exploratory data analysis, feature engineering, and feature selection.
• Build classification, regression, clustering, segmentation, recommendation, and forecasting models.
• Design and execute model validation, experimentation, and backtesting methodologies.
• Evaluate model performance and translate results into clear business recommendations.
• Develop reusable modeling frameworks and reproducible Python workflows.
• Work with data stored in Snowflake and transformations developed in dbt Cloud.
• Partner with ML engineers to containerize, deploy, monitor, and improve models in production.
• Support model retraining, performance monitoring, and continuous improvement.
• Translate business questions into analytical hypotheses, measurable objectives, and practical solutions.
• Communicate findings, assumptions, risks, and business implications to technical and non-technical stakeholders.
• Collaborate with data engineers, ML engineers, product teams, and business stakeholders.

What we expect:

• 6–8 years of experience in data science, applied machine learning, statistics, or a related field.
• Strong Python and SQL skills.
• Hands-on experience with traditional ML methods and libs (pandas, numpy, scikit-learn, pytorch).
• Strong knowledge of predictive modeling, statistical analysis, experimentation, and model evaluation.
• Experience designing validation and backtesting frameworks.
• Practical experience with Snowflake and cloud-based analytics platforms.
• Experience taking models from experimentation through production deployment.
• Familiarity with Docker, data engineering concepts, and MLOps practices.
• Strong analytical thinking, communication, stakeholder management, and problem-solving skills.

Nice to have:

• Experience developing models that generate customer-level insights or recommendations.
• Familiarity with AWS and dbt Cloud.
• Knowledge of causal inference, experimentation, or optimization methods.
• Exposure to generative AI, LLM-based applications, graph analytics, or RAG.
• Experience in pharmaceutical, biotechnology, healthcare, or life sciences environments.

You will develop predictive models and analytical solutions that generate actionable insights and support business decision-making. The role focuses primarily on traditional machine learning using structured datasets. You will own model development, experimentation, validation, and performance evaluation, while working with ML engineers to deploy successful models into production. A key objective is to build reusable modeling approaches that can be applied across different products and use cases.

• Competitive compensation
• Flexible working hours
• Continuous education, mentoring, and professional development programs
• A team with an excellent tech expertise
• Contract through the end of the year, with a possible extension based on project needs and performance.