Data Scientist

About The Role

The role drives the development and scaling of predictive models and advanced statistical analyses that power core decision-making engines and product features.

The data scientist will work with high-velocity, multi-modal datasets, bridging the gap between raw data infrastructure and productionized machine learning solutions to deliver measurable business impact.

Key Responsibilities

• Design, prototype, and scale machine learning models using Python, scikit-learn, and XGBoost to solve complex classification, forecasting, and personalization problems
• Build robust data pipelines and perform complex feature engineering using SQL, Pandas, and PySpark to prepare diverse datasets for model training
• Design and execute rigorous A/B testing frameworks to validate model performance, quantify business impact, and drive continuous optimization
• Collaborate with MLOps and backend engineering teams to package, deploy, and monitor models in production environments via containerized microservices
• Communicate complex technical findings, experimental results, and model limitations clearly to product and business stakeholders through interactive dashboards and reports

What We Are Looking For

• 3–6 years of professional experience as a Data Scientist or Machine Learning Engineer, with a track record of deploying models into production
• Strong proficiency in Python and statistical packages, alongside advanced SQL skills for querying massive relational and non-relational databases
• Deep understanding of statistical modeling, experimental design (A/B testing), hypothesis testing, and core machine learning algorithms
• Experience with cloud data warehouses and platforms such as Snowflake, BigQuery, AWS, or GCP
• BS, MS, or PhD in Computer Science, Statistics, Applied Mathematics, Economics, or a related quantitative field
• Bonus: Experience with deep learning frameworks (PyTorch, TensorFlow) or deploying models using modern MLOps tools like MLflow or Prefect