Senior Data Scientist - Pune

Job Description

Job Description:

Notice Period: Immediate

Job Summary-

The ideal candidate is innovative, analytical, and a collaborative team player with a proven track record in end-to-end AI/ML project delivery, including data processing, modeling, and model deployment.

Mandatory: Forecasting and Prediction (mostly using timeseries data)Good experience on regression, deep learning and reinforcements.

Ability to work independently with complete hands on.

Key Responsibilities:

• Model Development: Design, build, and implement scalable machine learning models, including supervised and unsupervised learning, deep learning, and natural language processing (NLP) models, to solve a range of business problems.
• End-to-End Project Execution: Lead the entire lifecycle of AI/ML projects, from initial research, data processing, and model development to validation, deployment, and continuous monitoring.
• Advanced Analytics and Insights: Leverage AI and ML techniques to perform deep analyses and generate actionable insights that inform business decisions.
• Data Engineering Collaboration: Work closely with data engineering teams to design and structure data pipelines, ensuring data quality, consistency, and accessibility for model training and evaluation.
• Deployment and Optimization: Deploy machine learning models in production environments, ensuring optimal performance and scalability. Monitor model accuracy and relevance over time and retrain or refine models as necessary.
• Cross-Functional Collaboration: Partner with stakeholders across various departments (e.g., product, marketing, operations) to understand business needs and define AI/ML solutions that align with company goals.
• Mentorship: Mentor junior data scientists, providing guidance on best practices in AI/ML methodologies, data science tools, and coding standards.
• Documentation and Communication: Create comprehensive documentation of models, processes, and project outcomes. Communicate technical concepts and insights effectively to both technical and non-technical stakeholders.

• Experience: Minimum 2 to 5 years of experience in data science, with a focus on AI/ML applications.
• Technical Skills:
• Machine Learning & AI: Proficiency in a wide range of ML algorithms, such as regression, classification, clustering, decision trees, neural networks, and deep learning architectures (e.g., CNNs, RNNs, GANs).
• Programming: Strong programming skills in Python, R, or Scala; experience with ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
• Data Processing: Experience in data wrangling, cleaning, and feature engineering; familiarity with SQL and data processing frameworks (e.g., Apache Spark).
• Deployment: Experience deploying models using tools like Docker, Kubernetes, and cloud services (AWS, GCP, or Azure).
• Statistics & Mathematics: Strong foundational knowledge in statistics, probability, and mathematical concepts used in AI/ML.
• Visualization: Proficiency in data visualization tools such as Tableau, Power BI, or matplotlib for conveying complex analyses.

Preferred Qualifications:

• Experience with Big Data: Familiarity with big data tools (e.g., Hadoop, Hive) and distributed computing.
• Natural Language Processing (NLP): Hands-on experience in NLP techniques (e.g., text mining, sentiment analysis, transformers).
• Time-Series Analysis: Expertise in analyzing and forecasting time-series data.
• MLOps: Familiarity with CI/CD pipelines for ML, model versioning, and performance monitoring.
• Leadership: Experience leading cross-functional project teams or managing data science projects in a production setting.

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