The data scientist is expected to have expertise of Advanced Analytics techniques that are traditionally applied to structured data, as well as deep understanding of Natural Language Processing and Machine Learning techniques such as Deep Learning and GenAI for unstructured content, so as to enable composition of holistic cognitive solutions. The candidate is expected to be well versed in one of the following areas of Natural Language Processing, Image Processing, Video Processing, Voice Processing, Q&A assistant solutions and other forms of GenAI solutions. The candidate is expected to have knowledge and/or experience in the following skills with focus on Data Science: Machine Learning, Git, Agile, SQL, Python, R, Predictive Modelling, Algorithms, Web Scraping, TensorFlow, Deep Learning, Statistics and Natural Language Processing / GenAI and Agentic AI. This will include the following:

• Experience of formulating a problem statement and implementing analytical solutions by understanding available data and functional requirements.
• High proficiency in algorithms used for structured and unstructured data analysis
• Expert-level proficiency in statistical/ML predictive techniques such as Regression, Random Forest, Decision Tree, SVM etc or Experience in Probabilistic techniques, Reinforcement learning, Deep Learning- RNN, LSTN, NLP and Interactive AI (Chatbots)
• Expert-level proficiency in Foundational s models / LLMs, Lang chain, Vector DB, Python or data science stack.
• Operating knowledge in any one of the of cloud computing platforms (IBM, AWS, Google, Azure) and its data science stack
• RDBMS and data management concepts as well as fluency in SQL scripting.
• Experience in implementing distributed computing using Python
• Experience in Agile Delivery and version management

The ideal candidate will have 7-12 years of experience in designing, GenAI, Deep Learning, ML/statistical solutions to complex business problems at scale; developing & testing modular, reusable, efficient and scalable code to implement those solutions. The skills include strong coding, statistical, mathematical, predictive modelling, and business strategy skills and will need to be able to effectively communicate orally and visually — what all of the data means in the context of our business.

Required Technical and Professional Expertise

• BTech (with 7+ years of relevant experience) or Masters (with 6+ years of relevant experience) in Operations Research, Applied Mathematics/ Statistics/ Econometrics, Electrical or Systems Engineering, Physics or similar highly quantitative field
• Strong ability to transform business requirements into data science formulations and implement the solutions in an efficient and scalable fashion
• Sound understanding of data science concepts, model development & performance tuning processes as well as coding, version control and CI/CD best practices
• Demonstrated extensive experience in building and deploying production quality models in a live digital environment using data pipelines and ML Ops frameworks including handling model drift, retraining and version control lifecycle
• Highly skilled in Python and various data science related libraries of Python including TensorFlow, Keras, Sci-Kit Lean, Pandas, Numpy and PySpark
• Experience in Convolutional Neural Network / Computer Vision projects using TensorFlow, PyTorch and leveraging public / open-source libraries (VGG16, ImageNet, YOLO, OpenCV, etc) as well as ability to tweak, modify these CNN architectures when required for a specific business problem.
• Demonstrated ability of scoping, executing and scaling multiple data science deliverables on their own
• Must have worked in developing and deploying models using more than one cloud platform (AWS, Azure, GCP, IBM)
• Ability to handle multiple projects as an individual contributor and as a lead / mentor to other team members managed directly or indirectly on a project / assignment
• Excellent interpersonal and stakeholder management skills including ability to interact and present to senior stakeholders

Preferred Technical and Professional Expertise

• None