AL/ML Engineer

Position: Data Scientist

About the Role

Data Scientist

Key Responsibilities

Analyze large, complex datasets to uncover actionable insights and trends.Design, develop, and deploy machine learning models for classification, regression, clustering, or recommendation use cases.Translate business problems into analytical frameworks and solutions.Perform feature engineering, model selection, hyperparameter tuning, and performance evaluation.Communicate findings clearly through visualizations, dashboards, and presentations to both technical and non-technical stakeholders.Collaborate with data engineers and ML engineers to productionize models and ensure data pipeline integrity.Conduct A/B tests and experiments to validate hypotheses and measure impact.Contribute to the development of a centralized knowledge base for data science solutions, reusable code, and templates.Stay current with advances in data science, machine learning, and statistics.

Required Qualifications

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.3+ years of professional experience in data science or a related role.Proficient in Python and data science libraries such as Pandas, NumPy, scikit-learn, XGBoost, and/or TensorFlow/PyTorch.Strong analytical and statistical skills, including regression, classification, hypothesis testing, and clustering.Experience with SQL and working with relational or non-relational databases.Hands-on experience with data visualization tools (e.g., Matplotlib, Seaborn, Plotly, Power BI, or Tableau).Experience building end-to-end ML workflows and working with version control systems like Git.

Preferred Qualifications

Familiarity with MLOps tools and practices (e.g., MLflow, DVC, Airflow).Experience with cloud platforms such as AWS, GCP, or Azure.Understanding of data warehousing and big data technologies (Spark, Hadoop, BigQuery, Redshift).Background in a domain like fintech, healthtech, e-commerce, or marketing analytics.Experience with experimentation and causal inference techniques.