Data Scientist

Responsibilities

• Analyze large datasets to identify trends and generate actionable insights for decision making and operational efficiency.
• Communicate findings through visualizations and clear storytelling.
• Develop and deploy machine learning models for forecasting, optimization, and risk analysis.
• Apply statistical and machine learning techniques to solve complex business problems.
• Prepare data through extraction, cleaning, preprocessing, and feature engineering to ensure data quality and integrity for reliable model performance.
• Deploy models into production and monitor performance for drift or degradation, continuously improving models based on feedback and new data.
• Work with business teams to translate requirements into analytical solutions.
• Collaborate with Data Engineering and Data Governance teams for end‑to‑end delivery.
• Ensure all AI and machine learning activities comply with governance, privacy, and regulatory requirements by following lifecycle processes, risk classification, and using approved governed data.
• Implement responsible and secure AI practices, including bias and fairness checks, explainability, model documentation, auditability, and collaboration with Cybersecurity and Data Governance teams.

• Bachelor's or Master's Degree in Data Science, Artificial Intelligence (AI), Statistics, Computer Science, or a related field.
• Minimum 5+ years of experience in Data Science, Machine Learning, Artificial Intelligence, or Advanced Analytics.
• Proven experience developing, deploying, and maintaining machine learning models in production environments.
• Experience working with large‑scale datasets and cloud‑based analytics platforms (preferred).

• Strong programming skills in Python (preferred) or R.
• Experience with large‑scale data processing using SQL, Spark, or similar technologies.
• Hands‑on experience with machine learning frameworks such as Tensor Flow, PyTorch, and Scikit‑learn.
• Experience in end‑to‑end machine learning model development, validation, deployment, and optimization.
• Knowledge of MLOps practices, including CI/CD pipelines, model monitoring, version control, and automated retraining.
• Understanding of data engineering concepts, data pipelines, and cloud data platforms, preferably Microsoft Azure and Databricks.
• Strong knowledge of statistical analysis, predictive modeling, feature engineering, and data preprocessing techniques.
• Experience using data visualization tools such as Power BI and Tableau to communicate analytical insights.
• Understanding of data governance, data quality management, privacy regulations, and AI governance principles.
• Strong analytical thinking, problem‑solving, and critical reasoning skills.
• Excellent communication, presentation, and stakeholder management skills.
• Ability to work collaboratively with cross‑functional teams in an agile environment.
• Strong organizational skills with the ability to manage multiple priorities and deliver high‑quality outcomes within deadlines.