Data Scientist

• Collaborate with cross-functional teams and clients to define data-driven solutions to real-world business problems.

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  Design and develop machine learning models (classification, regression, clustering, recommendation systems, etc.) using structured and unstructured data.

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  Conduct thorough exploratory data analysis (EDA) and statistical testing to identify patterns, trends, and actionable insights.

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  Perform feature engineering, data transformation, and selection to optimize model performance.

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  Evaluate, validate, and fine-tune models using techniques like cross-validation, A/B testing, and performance metrics (e.g., F1-score, ROC-AUC, RMSE).

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  Prepare end-to-end pipelines for model development, training, validation, and testing using Python and ML libraries (e.g., Scikit-learn, XGBoost, TensorFlow).

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  Deploy ML models to production using Flask, FastAPI, or cloud-based solutions (e.g., Azure ML, AWS Sagemaker, GCP AI Platform).

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  Monitor model performance post-deployment and implement re-training strategies as needed.

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  Work on NLP, computer vision, or time-series forecasting projects as per client requirements.

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  Stay up to date with the latest developments in Data Science, ML, and AI, and proactively suggest innovative solutions for business problems.

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  Create clear documentation and present complex model outputs and insights in a simple, interpretable manner to both technical and non-technical stakeholders.

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  Contribute to the standardization of data science frameworks, reusable assets, and best practices across projects.

Requirements

• 4 to 5 years of hands-on experience in a Data Scientist role

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  Strong proficiency in Python Libraries required for ML (NumPy, Pandas, Scikit-learn, Tensorflow, Pyspark etc.)

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  Good experience with SQL and working with relational databases

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  Experience in building and evaluating predictive models and other machine learning models (supervised and unsupervised learning etc.)

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  Knowledge of EDA, feature engineering, and data preprocessing

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  Experience with Data Visualization - Power BI, Tableau, or Python-based visualization libraries

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  Experience working on cloud platforms (Azure, AWS, or GCP) is preferred

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  Familiarity with model deployment techniques (Flask, FastAPI, Docker, MLflow)

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  Strong communication skills and experience working in client-facing environments

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  Ability to manage multiple projects and meet tight deadlines

Benefits

• Working hours: 10:00 AM – 7:00 PM

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  Working days: 5 days a week (plus 1st & 3rd Saturdays working)

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  Medical Insurance coverage for employees

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  Provident Fund (PF) facility

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  Quarterly parties and yearly outings/trips for team bonding

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  Regular check-ins with leadership for growth and feedback

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  Recognition awards to celebrate high performance