Junior Data Science Engineer - Predictive Modeling & NLP

What role will you play in the team

As a Junior Data Science Engineer specializing in Predictive Modeling and Natural Language Processing, you will be an integral part of our core analytics ecosystem. You will collaborate closely with senior data scientists and machine learning engineers to design, build, and deploy robust algorithmic pipelines that extract actionable intelligence from complex, unstructured textual datasets. Your focus will be on translating ambiguous business problems into mathematically rigorous machine learning frameworks, driving predictive accuracy, and optimizing feature engineering pipelines for production-level deployment across enterprise systems in India.

What you will do

You will actively develop, test, and refine supervised and unsupervised machine learning models using Python and scikit-learn. Your daily responsibilities include cleaning and structuring large-scale datasets, executing exploratory data analysis, and querying relational databases using SQL and PostgreSQL to retrieve core operational metrics. Additionally, you will experiment with Large Language Models and Retrieval-Augmented Generation architectures, fine-tuning embedding spaces and leveraging vector databases like MongoDB to build context-aware AI search tools and automated document processing pipelines.

Key responsibility

• Design, train, and evaluate machine learning models utilizing scikit-learn, PyTorch, and TensorFlow to solve high-impact classification and regression challenges.
• Extract, transform, and load large datasets by writing complex SQL queries, optimizing PostgreSQL database schemas, and managing unstructured data in MongoDB.
• Implement Natural Language Processing workflows, including tokenization, sentiment analysis, and semantic search using state-of-the-art transformer architectures.
• Integrate pre-trained Large Language Models via OpenAI API and Claude API to automate text summarization, entity extraction, and customer intent classification.
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  Build scalable Retrieval-Augmented Generation systems to enhance knowledge retrieval accuracy and minimize hallucinations in enterprise chatbot deployments.
• Conduct rigorous statistical hypothesis testing, feature selection, and dimensionality reduction to improve model performance and generalization.
• Collaborate with MLOps teams to containerize models, monitor data drift, and deploy production-ready inference endpoints using RESTful APIs and webhooks.

Required Qualification and Skills

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related quantitative field from an accredited institution.
• 0-1 years of hands-on professional or academic experience in building, training, and evaluating machine learning models using Python and related libraries.
• Strong foundational knowledge and practical coding experience in Python, along with proficiency in libraries such as scikit-learn, pandas, NumPy, and PyTorch.
• Demonstrated expertise in Database Management, SQL, PostgreSQL, and MongoDB for querying, aggregating, and structuring relational and non-relational datasets.
• Familiarity with modern AI concepts including Large Language Models, Prompt Engineering, Retrieval-Augmented Generation (RAG), and API integrations.
• Solid understanding of statistical analysis, probability distributions, linear algebra, and data preprocessing methodologies.
• Excellent analytical, problem-solving, and communication skills with the ability to articulate complex algorithmic results to technical and non-technical stakeholders.

Benefits Included

• Competitive starting annual compensation package with performance-linked appraisals and annual performance bonuses.
• Comprehensive health, medical, and life insurance coverage extending to immediate family members.
• Flexible working hours with standard Work From Office infrastructure in a modern, tech-enabled corporate office.
• Dedicated annual learning and development budget for pursuing specialized certifications in AI, Data Science, and Cloud Technologies.
• Regular technical workshops, hackathons, and cross-functional upskilling sessions led by industry veterans.
• Generous paid time off, casual leaves, and maternity/paternity leave policies in compliance with industry standards.
• Well-stocked pantry, catered lunches, recreational zones, and corporate wellness programs including mental health support.

A Day in the Life

Your typical workday begins with a quick daily stand-up with the data science squad to review sprint goals and discuss ongoing model iteration results. You will spend your morning writing optimized SQL queries to extract fresh training data from PostgreSQL databases, cleaning the data, and engineering new predictive features. Post-lunch, you will dive into coding Python scripts to fine-tune a transformer-based NLP model and test its performance against a custom evaluation dataset. The afternoon involves integrating a newly optimized retrieval pipeline using vector embeddings and collaborating with the API development team to expose the model endpoint via webhooks. You wrap up your day by documenting your code experiments in Git and preparing a brief status report for the engineering lead.