Junior Data Science Engineer - TensorFlow And Predictive Modeling

What role will you play in the team

You will play a critical foundational role within our core data science and machine learning division based in Hyderabad. Your primary objective will be to design, build, and deploy robust predictive models and deep learning architectures using advanced frameworks like TensorFlow and scikit-learn. You will collaborate directly with senior data scientists and machine learning engineers to translate complex business requirements into scalable algorithmic solutions, driving data-informed decision-making across enterprise operations.

What you will do

Your day-to-day responsibilities will involve writing clean, production-grade Python code to preprocess massive datasets, engineer meaningful features, and train advanced predictive models. You will experiment with various neural network architectures using TensorFlow and Keras, optimize hyperparameters, evaluate model performance metrics, and deploy these models into production environments. Additionally, you will participate in rigorous code reviews, document your methodology, and continuously monitor model drift to ensure sustained prediction accuracy.

Key responsibility

• Develop, train, and evaluate machine learning models using TensorFlow, Keras, and scikit-learn to solve complex predictive classification and regression challenges.
• Perform comprehensive exploratory data analysis (EDA) on large-scale datasets to uncover hidden patterns, anomalies, and actionable insights.
• Implement data preprocessing pipelines, handle missing values, perform feature scaling, and conduct dimensionality reduction techniques.
• Collaborate with data engineering teams to optimize feature stores and ensure seamless data ingestion from various database management systems.
• Conduct hyperparameter tuning, cross-validation, and performance optimization to maximize model accuracy, precision, recall, and F1-score.
• Deploy trained machine learning models as RESTful APIs using microservices architecture for real-time inference integration.
• Maintain comprehensive documentation of model architectures, training procedures, and validation results for internal auditing and compliance.

Required Qualification and Skills

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related quantitative field.
• Minimum 0 to 1 year of hands-on professional or academic experience building and deploying machine learning models.
• Proficiency in Python programming language with deep expertise in scientific computing libraries including NumPy and Pandas.
• Demonstrated practical experience utilizing machine learning frameworks such as TensorFlow, Keras, and scikit-learn.
• Strong foundational knowledge of statistical analysis, probability theory, linear algebra, and multivariate calculus.
• Familiarity with database management systems, SQL querying, and extracting data from relational databases.
• Excellent problem-solving abilities, strong verbal and written communication skills, and the capacity to work effectively within agile cross-functional teams.

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Benefits Included

• Competitive entry-level annual compensation package aligned with industry standards and performance incentives.
• Comprehensive health insurance coverage including medical, dental, and vision benefits for you and your dependents.
• Flexible working hours with remote work options supported by robust collaboration infrastructure.
• Generous paid time off, public holidays, and dedicated wellness days.
• Continuous learning stipend for online courses, technical certifications, and conference attendances.
• Access to state-of-the-art computing hardware, cloud resources, and enterprise AI development tools.

A Day in the Life

Your typical day begins with a morning stand-up meeting with your pod to discuss sprint goals and review yesterday's model evaluation metrics. You will spend the morning writing Python scripts in Jupyter notebooks to clean a new batch of unstructured data and engineer features for an ongoing customer churn prediction project. After lunch, you will configure a deep neural network using TensorFlow, initiate training runs on GPU instances, and analyze the resulting validation loss curves. The afternoon concludes with a collaborative session with data engineers to streamline API endpoints for model deployment.