Lead Data Scientist - Deep/Machine Learning (8-14 yrs)

Responsibilities :

- Ability to build and lead a team of data scientists.

- You would be required to identify, develop, and implement the appropriate statistical modeling and ML/Deep learning models to create new, scalable solutions that address business challenges across industry domains.

- Create business stories and presentations through Data Inferential Analysis

- Define, develop, maintain, and evolve data models, tools, and capabilities.

- Communicate your findings to the appropriate teams through visualizations

- Collaborate and communicate findings to diverse stakeholders

- Required Implementation Experience (any 3 or above) : Clustering, Customer Segmentation & Targeting, Propensity Modeling, Churn Modeling, Lifetime Value Estimation, Forecasting, Modeling Response to Incentives, Marketing Mix Optimization, Price Optimization.

- Good to have exposure in Object detection/Image recognition, natural language processing, Sentiment Analysis, Topic Modeling, Concept Extraction, Recommender Systems, and text classification.

- Experienced in agile methodology for delivering on project milestones.

- Need to have command on verbal, presentation, and written communication skills.

Preferred Qualifications :

- Bachelors/Masters/PhD degree in Math, Computer Science, Information Systems, Machine Learning, Statistics, Applied Mathematics, or related technical degree.

- 8+ years total experience with a minimum of 3 years of experience in a related position as a data scientist or ML engineer building predictive analytics and machine learning solutions for various types of business problems.

- Advanced knowledge of Statistical Modeling, Machine Learning Algorithms, and Python Programming

- Strong individual planning and project management skills, able to work on more than one

responsibilities through right prioritization

- Self-motivated and driven to deliver agreed results on-time

- Ability to convert analytical output into clear, concise, and persuasive insights & recommendations for technical & non-technical audience