Pyspark Data Science Job in Rarr Technologies

Job Description :

Understand the customer use-cases

Data validation 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

2-5 years of experience in developing Python codes/modules in BFSI, Healthcare, CPG retail, Manufacturing or e-commerce domain

A bachelor s/ Master s degree in Science, Engineering, Operation Research

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

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

API Creation & experience in relevant testing is must like postman.

working with microservices and GIT branching is must.

Strong experience in API query using user input.

Code Deployment & API Testing. Experience in CI/CD deployment structure.

Knowledge about various cloud environment (AWS is Must)

Experience in developing functions in python.

Experience in EQL (Elastic Query Language) & MQL (Metaphor Query Language)

Basic stat (distribution, Monte Carlo, other types of simulations, basics of regressions)

Experience in different data manipulation steps like (Group by, Pivot table, Merge, Join, Set operations, Regular expression etc.)

Strong analytical thinking and hands-on experience in problem solving

A good understanding of SQL and no-SQL databases

Strong verbal and written communication skills

It will also be good to have

A master s 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

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