Lead Data Scientist

Job Description

This role involves working with senior stakeholders in understanding and identifying the business requirements and needs in the areas of Big Data Analytics and Artificial Intelligence. Scanning the fast paced external and internal environments in these areas, developing futuristic ideas that would benefit the firm and leading implementation of mathematical/AI models/algorithms/solutions to bring those ideas to life. Specific experience in AI areas like deep learning, genetic algorithms will be an advantage.Individual in this role is expected to work with multiple stakeholders and teams, have excellent knowledge and experience of recent developments and work in a self-directed manner in ambiguously defined need areas.

• Improve customer experience, top line and bottom line of the business by identifying growth and efficiency opportunities
• Analyze structured and unstructured data using data mining/statistical/machine learning/deep learning tools, Data visualization, Digital/ Web analytics tools and techniques and story telling.
• Managing business partner / stakeholder engagement
• Participating in / leading interactions with middle and senior level management

Required Candidate profile

• Independently reason and understand models in general from and probabilistic perspective
• Expert in handling various data types and structures: structured, unstructured, voice, static versus streaming data. Extensive prior experience in integrating data
• Possess extensive knowledge of and experience in applying data mining and machine learning, deep learning and Reinforcement learning techniques in a professional context
• Should have extensive experience with relational, NO-SQL and Graph databases
• Interpret, criticize, and debug neural networks (not just implement and evaluate accuracy)
• Expertise with Bayesian probabilistic modeling, familiar with probabilistic programming (e.g. Stan)
• Experience with Generative neural networks such as VAEs
• Proficiency in the entire Data Modeling life cycle - From exploratory data analysis to data cleaning to Feature Engineering to Model Building and Validation.
• Comfort with Tensorflow and PyTorch and associated tooling
• Exposure to natural language processing and associated tooling (Spacy, NLTK- Natural Language Toolkit)
• Familiarity with foundational model reasoning: bias-variance tradeoff, shrinkage/partial pooling
• Able to independently research and apply state-of-the-art deep learning and Bayesian modeling literature
• Communicate design decisions, tradeoffs, and findings to technical and non-technical stakeholders
• Expert in Python Programming preferably functional programming
• Experience in Building models in AWS environment
• Comfort with Git source control and repo-driven development - continuous integration / continuous deployment
• Able to organize code using functional programming concepts, experience with statically typed functional programming a plus
• Comfortable working with data platforms independently - able to write REST clients, SQL, PySpark, as needed.
• Exposure to containerization/Docker

• Familiarity with Kubernetes and exposure to Kubeflow
• Expertise in natural language processing
• Experience with Gaussian process models
• Advanced theory - optimization, differential geometry, statistical physics, category theory
• Advanced functional programming languages - Haskell, Idris and exposure to declarative programming concepts
• 4+ years of relevant experience with organizations known for cutting edge/ best-in-class applications of Advanced Analytics, Predictive Modeling and Artificial Intelligence.
• Masters Degree / PhD in a quantitative field (e.g., Computer Science, Economics, Engineering, Statistics, Mathematics, Finance, Operations Research)

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Keyskills :
analyticsbanking business intelligenceclustering customer relationsbig data analytics exploratory data analysisnatural language