Walk-in || Data Science Analyst

Required Skills & Expertise Core Data Science & AI

• Strong hands-on expertise in Python, which is the primary language used for AI/ML development.
• Proven experience building production-grade AI/ML solutions using Python (NumPy, Pandas, Scikit-learn, SciPy).
• Solid foundation in statistics, probability, linear algebra, and optimization.
• Experience with machine learning techniques: regression, classification, clustering, time series forecasting, anomaly detection.
• Exposure to deep learning frameworks such as TensorFlow or PyTorch.
• Willingness and ability to learn and work with other programming languages (e.g., Java, Scala, SQL, or platform-specific languages) as required by enterprise platforms and integrations. Data & Platforms
• Experience working with SQL and NoSQL databases.
• Understanding of data pipelines, ETL/ELT processes, and feature engineering workflows.
• Hands-on exposure to cloud platforms (AWS and/or Azure) for data science and ML workloads.
• Familiarity with model deployment, monitoring, versioning, and MLOps practices. Engineering & Collaboration
• Strong coding practices, version control (Git), and reproducible experimentation.
• Ability to translate business problems into analytical and ML solutions.
• Comfortable working in multi-language, enterprise technology stacks, with a Python-first mindset for AI/ML.

Strong collaboration skills across engineering, product, and business teams.

Preferred Certifications

• AWS Certified Machine Learning Specialty
• Microsoft Certified: Azure Data Scientist Associate
• Google Professional Machine Learning Engineer
• TensorFlow Developer Certificate (good to have)
• Certifications in AI, Data Engineering, or Cloud Architecture are a plus

Good-to-Have Skills

• Exposure to GenAI, LLMs, Vector Databases, RAG, and Prompt Engineering.
• Experience with real-time analytics, streaming data, or IoT data.
• Familiarity with enterprise domains such as supply chain, manufacturing, or operations analytics.
• Understanding of data governance, privacy, and responsible AI principles. Ideal Candidate Profile
• 5+ years of hands-on experience in data science and applied machine learning.
• Strong Python-based AI/ML implementation experience with real-world production exposure.
• Proven ability to take models from experimentation to production.
• Demonstrates learning agility and adaptability to evolving enterprise platforms and technologies.
• Passion for data, AI-driven problem solving, and continuous learning.