Job Description :

Must Have Skills :

Python, SQL, Statistics, Probability, Pandas, NumPy, Scikit-learn, Exploratory Data Analysis (EDA), Data Cleaning, Feature Engineering, Hypothesis Testing, A/B Testing, Regression, Classification, Clustering, Time Series Analysis, Data Visualization, Matplotlib, Business Problem Solving, Model Evaluation, Communication Skills.

Good To Have Skills :

PyTorch, TensorFlow, XGBoost, LightGBM, NLP, Generative AI, Large Language Models (LLMs), Tableau, Power BI, Spark, Databricks, Snowflake, BigQuery, AWS, Microsoft Azure, Google Cloud Platform (GCP), MLflow, Causal Inference, Recommendation Systems, Optimization.

Experience Requirement : 2 yrs to 10 Yrs

Work Mode : From Office

Job Role : Full Time

Education :

B.E., B.Tech, M.Tech, MCA, M.Sc (Computer Science, Information Technology, Artificial Intelligence, Data Science) or equivalent practical experience

Shift Timings : 9.30 Am - 6.30 Pm

Key Requirements :

- Hands-on experience in Data Science, Machine Learning, Predictive Analytics.

- Strong proficiency in Python, including experience with data manipulation, statistical analysis, machine learning, and model development using libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch and related frameworks.

- Solid understanding of statistics, probability theory, hypothesis testing, regression analysis, sampling techniques, and experimental design to support data driven decision making.

- Apply statistical and machine learning techniques to analyze large datasets, identify patterns, build predictive models, and generate actionable business insights.

- Experience in building REST APIs and integrating AI models into production environments.

- Strong understanding of statistics, machine learning algorithms, forecasting techniques, and optimization methods.

- Excellent analytical, problem-solving, and communication skills with the ability to work in cross-functional teams.

Good To Have :

- Experience with Generative AI technologies, Large Language Models (LLMs).

- Experience working with SQL and NoSQL databases, data warehouses, and big data technologies.

- Familiarity with cloud platforms such as AWS, and containerization technologies such as Docker and Kubernetes.

Roles & Responsibilities :

- Collaborate with business stakeholders, product managers, and engineering teams to translate business requirements into analytical solutions.

- Extract and integrate data from multiple sources through APIs, databases, and file-based interfaces.

- Analyze structured and unstructured data to identify trends, patterns, and actionable business insights.

- Perform data preprocessing, feature engineering, model development, deployment and model evaluation.

- Evaluate, fine-tune, and optimize AI/ML models to improve accuracy, performance, scalability, and reliability.

- Develop APIs and services for model deployment and integration with enterprise systems.

- Ensure data quality, governance, security, and compliance throughout the analytics lifecycle.

- Stay current with emerging AI, GenAI, machine learning, and data science technologies and recommend their adoption where appropriate.