Data Scientist - IT

Responsibilities and Key Result Areas

- Work with internal Business/Product units and external customers, engineering and research teams in building predictive models in healthcare and lifestyle areas

- Manipulate and analyze complex, high-volume, high-dimensional data from varying sources

- Setup, Analyze and interpret the results of product experiments. Answer questions by using appropriate statistical techniques on available data

- Communicate complex quantitative analysis in a clear, precise, and actionable manner

- Design, develop and implement solutions and implementations using R, Python and HSDP ML libraries

- Create/Leverage algorithms to extract Patterns from large data sets.

- Define and adhere to scalable, efficient processes for model development, model validation, model implementation and large scale data analysis.

- Develop metrics and prototypes that can be used to drive business decisions.

- Identify emergent trends and opportunities for future client growth and development

- Drive the collection of new data and the refinement of existing data sources

- Interface with stakeholders for Requirements understanding, architecture clarifications etc.

- Support in project estimation, planning and risk management activities. Identify and escalate risks in time

- Ensure compliance to the Quality Management System and regulatory requirements

Know-how & Skill Set:

- Bachelor's or Master's Degree in applied statistics, computer science, Applied maths, data mining, machine learning or operations research.

- Strong background in Maths (Linear Algebra, Stats, Linear/Quadratic programming, Calculus)

- Deep understanding of predictive modeling concepts, machine-learning approaches like clustering, classification, recommendation, kernel tricks, optimization algorithms and NLP

- Extensive experience solving analytical problems using quantitative approaches (e.g. Bayesian Analysis, Dimensional Data Representations, and Multi-scale Feature Identification, Dimensionality reductions, Neural Nets).

- Strong Proficiency with statistical analysis languages (e.g. R, Python)

- Ability to develop hypothesis, test them with experiments, Statistical analysis and predictive modelling

- Have done projects on real data (Both Structured and Unstructured )

- Min of 4-5 years of experience in delivering data science outcomes, solving complex analytical problems using quantitative approaches blending analytical, mathematical and technical skills.

- Good hold on data visualization and presentation.

- Strong passion for empirical research and for answering hard questions with data

- Preferable - Healthcare domain, Experience with big data tools (e.g., Spark, H20)