Lead Data Scientist

Role Purpose :

• Expert Data Scientist who can lead independent projects by building advanced algorithms.
• Should have strong technology background in using latest ML frameworks and solving business problems.
• The person should be able to conceptualize and build ML algorithms which can be deployed on scale.
• Expertise in advanced analytics tools and methods is a must like Python, Cloud, Real time deployment of algorithms.
• They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations.
• They must have a proven ability to drive business results with their data-based insights.
• They must be comfortable working with a wide range of stakeholders and functional teams.
• The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

Responsibilities for Lead Data Scientist :

• Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Develop custom data models and algorithms to apply to data sets.
• Use predictive modeling to increase and optimize customer experiences, revenue generation and other business outcomes.
• Coordinate with different functional teams to implement models and monitor outcomes.
• Develop processes and tools to monitor and analyze model performance and data accuracy.

Qualifications for Lead Data Scientist :

• Strong problem solving skills with an emphasis on product development.
• Experience using statistical computer languages (R, Python, Julia, etc.) to manipulate data and draw insights from large data sets.
• Experience working with and creating data architectures.
• Knowledge of a variety of machine learning techniques supervised, unsupervised and reinforcement learning (clustering, decision tree learning, artificial neural networks, etc.) and their real-world applicability advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
• Excellent written and verbal communication skills for coordinating across teams.
• A drive to learn and master new technologies and techniques.
• We’re looking for someone who has a Master’s or PhD in Statistics, Mathematics, Computer Science or another quantitative field, and is familiar with the following software/tools:
  • Coding knowledge and experience with several languages: Python/R/Julia
  • Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
  • Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
  • Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
  • Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
  • Experience analyzing data from 3rd party providers and data vendors: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.
  • Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
  • Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.
• Significant experience in analytics and business transformation projects in engineering industries in energy business is mandatory. Project experience should include that of individual contributor, team lead and business translator.
• Mining experience is preferred.

Qualifications :

• Essential: M.Tech / MBA / PG.Dip (Data Science / Machine Learning / Analytics).
• Desired: B. Tech Mining Engineering.

Experience :

• 10 + years of overall experience.
• 6 + years’ experience, in manipulating data sets and building statistical and ML models.