Data Scientist

Role & responsibilities

• Analyze data and generate analytical outputs to deliver insights, evaluate the hypothesis and complete root cause analysis of the business problem.
• Should have strong analytical skillset to design end to end solution leveraging the latest machine learning (ML) techniques, including data preparation, exploratory data analysis, feature engineering, model selection, model development, model evaluation, cross-validation, and deployment
• Effectively present the analytics approach and insights to a larger business audience.
• Solve business problems which involves:
• Brainstorm with clients and internal teams to define a problem
• Translate the business problem into an analytical problem
• Identify internal and external data requirements for solving the analytical problem
• Solving the analytical problem using concepts from mathematics, statistics, Artificial Intelligence and Machine learning
• Create, maintain and enhance artefacts that can help communicate the solution to clients like dashboards, power point decks, excel sheets etc.
• Mentor team and handle day to day project deliverables and timeline.
• Develop algorithms based on the problem statement & domain area. Design & develop new algorithms for optimization, recommendation, attribution, and emerging real-time consumer analytics.
• Establish mathematical models to represent specific business functions or consumer behavior.
• Programming of algorithms, and integration into the Kvantum data science framework.
• Provide Support and add new features in a marketing performance analytics automation platform. Work on the scalability and performance of the automation platform.
• Create proof of concepts to vet out new technologies.
• Influence design and architecture principles for new technology products.

Preferred candidate profile

For this position, you are mandatory to have:

• BE/BTECH - 2- 6 years of work experience in data science and statistical modeling.
• Good understanding of Statistics and ML Models
• Proficiency in SQL.
• Experience in Python
• Experience of working on large data.
• Team Handling and Client handling experience.
• Should be okay with consulting nature of work, multiple projects of short/long durations, client delivery, mix of business analytics and data science
• AIML experience & Statistics basics
• ML applications and use cases such as regressions, time series, forecasting, predictive modelling for customer targeting, churn prediction, risk identification, classification algorithms decision trees, random forest, clustering are preferred.

Perks and benefits