Data Scientist - Stastical Modelling - 7 To 10 Years

Experience working on complex problems in which analysis of situations or data requires an in depth evaluation of various factors. Ability to exercise judgment within broadly defined practices and policies in selecting methods, techniques, and evaluation criteria for obtaining results.

• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business.
• Preferred candidates will have experience applying analytics, machine learning, or statistical modeling in an insurance, financial services, healthcare, employee benefits, or similarly regulated business environment.

Applied experience with several of the following:

• Data Acquisition and Transformation: Acquiring data from disparate data sources using APIs, SQL and NoSQL. Transform data using SQL, NoSQL, and Python. Visualizing data using a diverse tool set including but not limited to Python and R.
• Model Deployment: Understanding of: MDLC (Model Development Life Cycle), CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness etc.), A/B testing.
• Statistics and Computing: Exceptional understanding of: Calculus, Multivariable Calculus, Linear Algebra, Differential Equations, Probability, Statistics, Applied Probability, Applied Statistics, Computer Science (Programming Methodologies), and Cloud. Knowledge of statistical techniques such as the use of descriptive, inferential, Bayesian statistics, time series analysis etc. to extract business insights and experimentation to solve business problems
• Machine Learning: Understanding of machine learning theory, including the mathematics underlying machine learning algorithms. Expertise in the application of machine learning theory to building, training, testing, and monitoring machine learning models. Understanding and expertise in NLP (natural language processing).
• Programming Languages: Python, R, SQL,