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.
Requirements
- 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.
We offer
- 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.
