Assistant Manager - Analytics

Responsibilities

As a part of the analytics team, you'll be responsible for designing and developing production ready codes to implement features for the in-house product.

Your job will require the following -

- Gather requirements, understand data across structures/dimensions

- Understand the customer use-cases

- Data pre-processing to arrive at the right data quality

- Develop python codes, Modules, generic functions, deliver and deploy the same

- Partner with NLP, engineering and business teams to implement production ready codes

- Contribute in building and deploying new use cases for the product

A successful candidate will potentially have

- 2-4 years of experience in developing Python codes/modules in BFSI, Healthcare, CPG retail, Manufacturing or e-commerce domain

- A bachelors degree in STEM discipline

- Proficiency in Python is must for data processing, Data and statistical techniques, EDA

- Familiarities with data processing and ML libraries like pandas, numpy, scikit-learn is a must

- A good understanding of SQL and no-SQL databases

- Familiarity in developing ETL logics and codes

- Strong analytical thinking and hands-on experience in problem solving

- Familiarity with Predictive models like Regression, Classification, Clustering and Forecasting

- Strong verbal and written communication skills

It will also be good to have

- A masters degree (or equivalent) in business analytics or business intelligence

- A knowledge of technology infrastructure, specifically, big data technologies - Elastic Search/ Spark / H20 etc.

- Familiarity with platforms like google cloud, AWS, Azure etc.

- Familiarity with Spark/Pyspark

- Hands-on experience on BI solutions like Tableau, Power BI, Qlik, ThoughtSpot, Answer Rocket, Looker etc.

- Basic understanding of machine learning, supervised and unsupervised: Forecasting, Classification, Data/Text Mining, NLP, Decision Trees, Random Forest

- Experience in statistical learning : Predictive & Prescriptive Analytics, Parametric and Non-parametric models, Regression, Time Series, Dynamic / Causal Model, Statistical Learning