Job brief: We are looking for a Data Scientist who will support our product, sales, leadership and marketing teams with insights gained from analysing company data. The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. 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.
Experience: 4-10+ years
Qualification: We’re looking for someone with experience manipulating data sets and building statistical models, has a Bachelors’/Master’s degree in Statistics, Mathematics, Computer Science or another quantitative field and is familiar with the following software/tools mentioned inline.
Strong problem solving skills with an emphasis on product development.
Experience using statistical computer languages (R, Python, SLQ, 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 (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
Proficiency in NLP
Excellent written and verbal communication skills for coordinating across teams.
A drive to learn and master new technologies and techniques.
Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
Mine and analyse data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
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 modelling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
Develop company A/B testing framework and test model quality.
Coordinate with different functional teams to implement models and monitor outcomes.
Develop processes and tools to monitor and analyse model performance and data accuracy. Software/Tools:
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, SQL, etc.
Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, etc.
Experience analysing data from 3rd party providers: 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.
Job Types: Full-time, Walk-In
Salary: Up to ₹600,000.00 per year
- Python: 5 years (Preferred)
- Statistics: 5 years (Preferred)
- SQL: 5 years (Preferred)
- Java: 5 years (Preferred)
- C++: 5 years (Preferred)
- Web services: 5 years (Preferred)
- Software testing: 5 years (Preferred)
- SAP BusinessObjects: 5 years (Preferred)
- Cloud computing: 5 years (Preferred)
- Database design: 5 years (Preferred)
- Data mining: 1 year (Preferred)
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