HomemPokket | Jobs | Data Scientist | BigDataKB.com | 10-02-22

mPokket | Jobs | Data Scientist | BigDataKB.com | 10-02-22

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Job Location: Bangalore

Bangalore, Bangalore, Karnataka, India

Data & Analytics
Job posted on
Jan 11, 2022
Employment type
Experience range
3 years – 5 years
Data Scientist

Company Profile:

is an app lending platform. mPokket provides instant personal loans to all the college students and recent graduates from college (got a job offer or started working as a professional). The aim of the company is to make students financially responsible and independent. The loan amount is sent via instant credit to the bank account or Paytm wallet. The user can start by borrowing small amounts and the borrowing limit will keep increasing over time with good usage. Consider us pocket money alternative for the rainy day or emergency cash need! Our revolutionary technology uses AI to process the loans.

About the role –

Also Explore All Data Science Jobs in United States Using The Easy Search Filters Below or Simply Scroll Down To Continue With Current Job/Post: For Job Detail & Apply Link, just hit the + Symbol before company name as shown below bigdatakb.com

As a Data Scientist, you will work as part of our Credit and Analytics Team with key stakeholders to leverage our data to drive operational efficiencies and provide key data-driven insights. You should have a strong problem-solving ability and a knack for statistical analysis. If you’re also able to align our data products with our business goals, we’d like to meet you.

Your ultimate goal will be to help improve our products and business decisions by making the most out of our data.

Key Responsibilities:

  • Ability to deal with ambiguity and competing objectives in a fast paced environment
  • Partner with Product and Operations teams to solve problems and identify trends and opportunities
  • Inform, influence, support, and execute our product decisions and product launches

Work across the following four areas:

Product Operations:

  • Designing and evaluating experiments
  • Monitoring key product metrics, understanding root causes of changes in metrics
  • Building key data sets to empower operational and exploratory analysis
  • Evaluating and defining metrics

Exploratory Analysis:

  • Building models of user behavior for analysis or to power production systems

Product Leadership:

  • Influencing product teams through presentation of data-based recommendations
  • Communicating state of business, experiment results, etc. to product teams
  • Spreading best practices to analytics and product teams

Data Infrastructure:

  • Working with databases such as MySQL, Big Query, Cockroach DB and MongoDB.
  • Automating analyses and authoring pipelines via SQL and Python-based ETL framework

Minimum Qualifications

  • Master’s degree in Computer Science, Operations Research, Econometrics, Statistics or related technical field with overall 3-5 years of relevant experience
  • Experience solving analytical problems using quantitative approaches
  • Experience communicating quantitative analysis results
  • Knowledge with relational databases and SQL
  • Development experience in any scripting language (PHP, Python, Perl, etc.)
  • Knowledge of statistics (e.g., hypothesis testing, regressions)
  • Experience manipulating data sets through statistical software (ex. R, SAS) or other methods

Preferred Qualifications

  • Experience working with distributed computing tools (MapReduce, Hadoop, Hive, etc.)
  • Experience working with large data sets
  • Experience manipulating and analysing high-volume, high-dimensionality data from varying sources

Technical Skills –

Must have –

  • Programming – Python (Preferred) / R
  • ML Models – i. Regression – Linear, Logistic, Multinomial, Mixed effect
  • Classification – Bagging & Boosting (Random Forest), Decision tree, SVM
  • Clustering – K-Means, hierarchical, DB-Scan
  • Time series – ARIMA, SARIMA, ARIMAX, Holt-Winters, Multi TS (VAR), UCM
  • Neural Networks (Deep learning), Naive Bayes
  • Excel and SQL
  • Dimensionality Reduction – PCA, SVD etc.
  • Optimization Techniques – Linear programming, Gradient Descent, Genetic Algorithm
  • Cloud – Understanding of Azure / AWS offerings, Setting up ML pipeline of cloud

Good to have –

  • Visualization – Tableau / Power BI / Looker / QlikView
  • Data management – HDFS, Spark, Advanced Excel
  • Agile Tools – Azure DevOps, JIRA
  • Pyspark
  • Big Data/Hive Database
  • IDE: Pycharm

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