Position: Data Scientist (TK50FT RM 4322)
Virtual Interviews on 14 & 15-Aug-26
The role requires a functional System Engineer (Business Analyst) mindset
Job Description
We are seeking an accomplished Senior Data Scientist with 5+ years of experience in designing, developing, and deploying data science solutions for enterprise use cases. The ideal candidate will lead initiatives around applied machine learning, statistical modeling, experimentation, causal inference, and production analytics. This role requires strong technical skills, sound judgment, and the ability to translate ambiguous business questions into measurable outcomes through data pipelines, models, and decision support systems.
Key Responsibilities
- Problem Framing & Analysis
- Work with business and product teams to define the right problem and the right success metric.
- Design and run experiments, A/B tests, and causal studies to measure impact.
- Apply statistical modeling to answer questions where machine learning is not the right tool.
Model Development
- Build and validate machine learning models for forecasting, classification, ranking, or recommendation.
- Evaluate trade-offs between model complexity, interpretability, and performance based on the business need.
- Collaborate with data engineers and ML engineers to move models from prototype to production.
Data & Pipeline Engineering
- Build and maintain data pipelines for ingestion, transformation, and feature engineering.
- Ensure data quality, reproducibility, and version control across analyses and models.
Communication & Collaboration
- Present findings clearly to both technical and non-technical audiences, with honest treatment of uncertainty.
- Review the work of other data scientists and mentor junior team members.
- Collaborate with product managers, engineers, and stakeholders to align data science work with business goals.
Required Skills & Experience : 5+ years of experience in applied data science, with examples of work that was used by the business.
Strong Expertise In
- Python and SQL for data analysis and modeling.
- Applied statistics, experimentation, and hypothesis testing.
- Machine learning in at least one area: forecasting, causal inference, NLP, or recommendations.
Experience With
- Data pipelines and ETL workflows.
- Production ML systems and model monitoring.
- Proven track record of delivering data science work that changed a business decision or outcome.
Preferred Qualifications
- Experience with cloud-native data platforms (Azure, AWS, or GCP).
- Knowledge of causal inference methods or large-scale experimentation platforms.
- Familiarity with MLOps practices.
- Strong communication and stakeholder management skills.
