Junior Data Science Engineer

What role will you play in the team

You will play a vital foundational role within our core Data Science, Analytics & Machine Learning team by supporting senior engineers in building, validating, and deploying robust predictive models and data pipelines. Your primary focus will be transforming raw datasets into actionable machine learning features, conducting statistical validation, and ensuring high-fidelity data preprocessing for various production-grade enterprise algorithms and analytical workflows across diverse business sectors.

What you will do

You will write clean, efficient, and modular Python scripts to clean and manipulate large datasets stored in relational and non-relational databases using SQL and MongoDB. Your daily routine will involve training foundational machine learning models using scikit-learn, building data visualizations in Power BI to present statistical insights, collaborating with data engineering units to optimize ETL workflows, and conducting hyperparameter tuning for optimization.

Key responsibility

• Develop and maintain scalable Python scripts for automated data extraction, cleaning, and exploratory data analysis across multiple enterprise databases.
• Design, build, and optimize structured relational database queries using SQL, PostgreSQL, and MySQL to support advanced analytical queries.
• Implement baseline machine learning algorithms utilizing scikit-learn for classification, regression, and clustering tasks based on historical business data.
• Construct interactive and dynamic data visualization dashboards using Power BI and Tableau to track key performance metrics and model accuracy.
  
• Participate in peer code reviews, document machine learning model architectures, and maintain version-controlled code repositories using collaborative development platforms.
• Assist senior data scientists in evaluating model drift, conducting feature selection, and streamlining model deployment pipelines for production environments.

Required Qualification and Skills

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related quantitative field of study.
• Demonstrated academic or professional proficiency in programming languages including Python, along with strong working knowledge of SQL databases.
• Hands-on experience implementing machine learning algorithms and data preprocessing pipelines using libraries such as scikit-learn and Pandas.
• Familiarity with data visualization tools like Power BI or Tableau for translating complex analytical findings into clear business dashboards.
• Solid foundational understanding of statistical analysis, probability distributions, hypothesis testing, and exploratory data analytics methodologies.
• Excellent collaborative communication skills, analytical problem-solving abilities, and a proactive attitude toward continuous technical learning in data science.

Benefits Included

• Comprehensive health insurance coverage extending to immediate family members with annual preventive health check-up packages included.
• Flexible working hours and robust work-from-office infrastructure designed to foster collaborative in-person innovation and team synergy.
• Dedicated annual learning and development budget allocated for professional certifications, technical conferences, and advanced skill-building courses.
• Performance-linked annual bonuses, structured appraisal cycles, and competitive provident fund contribution plans aligned with industry standards.
• Regular team-building events, wellness programs, fully stocked pantry, and modern ergonomic workspaces located in prime metropolitan hubs.

A Day in the Life

Your day begins with a collaborative stand-up meeting with your engineering pod to review ongoing model development tasks. You spend the morning writing optimized SQL queries to extract fresh training data, followed by preprocessing the dataset in Python using Pandas. In the afternoon, you train a classification model using scikit-learn, evaluate its precision-recall metrics, and update a Power BI dashboard to showcase performance trends to stakeholders.