Roles and Responsibilities:
 Own and execute analytics-related projects.
 Independently interact with internal/external clients to understand requirements and provide updates
for Proposal or Execution, as the case may be.
 Understand clients' business questions and develop solution architecture.
 Build predictive models and machine-learning algorithms
 Solve business problems by applying advanced Machine Learning algorithms and complex statistical
models to large volumes of data.
 Demonstrate strong thought-leadership and consult with product and business stakeholders to build,
scale and deploy holistic data science products after successful prototyping.
 Define an analytics plan and delivery schedule.
 Develop comprehensive models/codes for specific use cases (like segmentation, forecasting, prediction
key driver analysis, price elasticity, prediction ) that can be used in a productized form with 'no
requirement of manual intervention' once they are developed.
 Ensure end-to-end implementation of the developed modules on the products.
 Develop and distribute product strategies for analytics-related interventions
 Performs research and applies new techniques and concepts to solve problems
 Provide thought leadership, perform Advanced Statistical Analytics, and create insights into data to
provide the business actionable insights, identify trends, and measure performance that addresses
business problems.
 Collaborate with business and process owners to understand business issues, and with engineers to
implement and deploy scalable solutions, where applicable.
Desired Candidate Profile:
 A Master s or higher degree in Computer Science, Statistics, Mathematics, or related disciplines
 10+ years of experience with ETL,data processing, data programming, and data analytics
 Experience with Big Data processing (Spark/Bigquery / Hive/ Hadoop/ HDFS)
 Experience in R, SQL, and Python;
 Experience in working with tools over AWS / Azure on big data analysis.
 Experience in data mining and statistical analysis
 Proficiency in machine learning algorithms such as decision trees, support vector machines,
 Gradient Boosting Machines (GBM), Random Forest, Regularized regression models, time series
forecasting, anomaly detection, etc.
 Strong understanding of probability and statistical models (generative and descriptive models)
 Experience in pattern recognition and predictive modeling
 Understanding of machine-learning and operations research
 Ability to run experiments scientifically and analyze results.
 Understanding of machine-learning and operations research.
 Ability to effectively communicate technical concepts and results to business audiences in a
comprehensive manner.
 Experience with Performance Engineering including testing, tuning, and monitoring tools will be added on
Key behavioral attributes
 Proactive and highly organized, with strong time management and planning skills
 Able to meet tight deadlines and remain calm under pressure
 Experience working with key stakeholders at senior levels.
 Demonstrable relationships with IT vendors are a plus.
 Strong Leadership, professional attitude – and leading by example
 Passionate about IT and a good understanding of emerging IT technologies are important
 Ability to multi-task and stay organized in a dynamic work environment
 Analytical and inquisitive, with excellent attention to detail