Data Scientist - Healthcare/Life Sciences

Position_Description:

to discover how they can leverage their data using advanced and sophisticated AI/ML algorithms for which we are looking- for Data Scientists with the capability to work on independent statistical and machine learning research/ projects. If you are a problem solver with a curiosity for exploring new techniques and technologies in AIML space, then we would like to talk with you.

Job Responsibilities:

- Ability to understand a problem statement and implement analytical solutions & techniques independently with independently/proactively/thought-leadership.

- Work with stakeholders throughout the organization to identify opportunities for leveraging company/client data to drive business solutions.

- Fast learner: ability to learn and pick up a new language/tool/ platform quickly.

- Conceptualize, design, and deliver high-quality solutions and insightful analysis.

- Conduct research and prototyping innovations; data and requirements gathering; solution scoping and architecture; consulting clients and client facing teams on advanced statistical and machine learning problems.

- Collaborate and Coordinate with different functional teams(engineering and product development) to implement models and monitor outcomes.

- Ability to deliver AIML based solutions around a host of domains and problems, with some of them being: Customer Segmentation & Targeting, Propensity Modeling, Churn Modeling, Lifetime Value Estimation, Forecasting, Recommender Systems, Modeling Response to Incentives, Marketing Mix Optimization, Price Optimization

Experience Required:

- Expert level proficiency in at least one of R and Python.

- Ability to create efficient solutions to complex problems. Strong skills in data-structures and ML algorithms.

- Experience of working on end-to-end data science pipeline: problem scoping, data gathering, EDA, modelling, insights, visualizations, monitoring and maintenance.

- Problem-solving: Ability to break the problem into small parts and applying relevant techniques to drive required outcomes.

- Intermediate to advanced knowledge of machine learning, probability theory, statistics, and algorithms. You will be required to discuss and use various algorithms and approaches on a daily basis.

- We use regression, Bayesian methods, tree-based learners, SVM, RF, XGBOOST, time series modeling, dimensionality reduction, SEM, GLM, GLMM, clustering, Deep learning etc. on a regular basis. If you know few of them you are good to go.