Data Scientist

We are seeking a data-driven and analytical Data Scientist with minimum 3 years of professional experience to join our growing Data Science team. In this role, you will bridge the gap between complex data and strategic business decisions. You will collaborate closely with product, engineering, and business teams to design, build, and deploy predictive models, extract actionable insights, and build data products that drive measurable impact.

The ideal candidate has a strong foundation in statistical analysis, machine learning algorithms, and software development practices, combined with a passion for solving open-ended business problems.

Key Responsibilities

• Model Development: Design, build, evaluate, and iterate on machine learning models (supervised and unsupervised) to solve key business challenges such as [e.g., churn prediction, recommendation engines, fraud detection, demand forecasting].
• Data Pipelines & ETL: Clean, preprocess, and feature-engineer large-scale structured and unstructured datasets from disparate sources to ensure high-quality inputs for modeling.
• Insights & Analytics: Perform exploratory data analysis (EDA) and apply advanced statistical methods (A/B testing, regression analysis, hypothesis testing) to uncover trends and patterns.
• Collaboration & Communication: Translate complex analytical findings into clear, actionable insights and visualizations for both technical and non-technical stakeholders.
• Production Deployment: Partner with Data Engineers and MLOps to deploy models into production environments and monitor their performance over time.
• Team Contribution: Actively participate in code reviews, share best practices, and contribute to the team's internal data science libraries and documentation.

Required Qualifications & Skills

• Experience: Minimum of 3 years of professional, hands-on experience working as a Data Scientist or in a highly quantitative analytical role.
• Education: Bachelors or masters degree in computer science, Data Science, Statistics, Applied Mathematics, Economics, or a related quantitative field.
• Programming: Strong proficiency in Python (preferred), including libraries like Pandas, NumPy, Scikit-Learn, and SciPy.
• SQL: Advanced proficiency in writing clean, optimized SQL queries to extract and manipulate data from relational databases.
• Machine Learning: Solid understanding of core machine learning concepts (e.g., linear/logistic regression, decision trees, random forests, gradient boosting, clustering, evaluation metrics).
• Cloud & Tools: Experience working within cloud environments (AWS, Google Cloud, or Azure) and version control tools (Git).