Machine Learning/ Devops Consultant

Company: Synechron
Location: Pittsburgh, Pennsylvania, United States
Type: Full-time
Posted: 01.JAN.2021
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Summary

About Synechron: ( ) Accelerating Digital for Banks, Asset Managers, and Insurance companies. Headquartered in New York and with 22 office...

Description

About Synechron: ( )

Accelerating Digital for Banks, Asset Managers, and Insurance companies.

Headquartered in New York and with 22 offices around the world, Synechron is a leading Digital Transformation consulting firm and is working to Accelerate Digital initiatives for banks, asset managers, and insurance companies around the world. Synechron uniquely delivers these firms an end-to-end Digital, Consulting and Technology capabilities with expertise in wholesale banking, wealth management and insurance as well as emerging technologies like Blockchain, Artificial Intelligence, and Data Science. This has helped the company to grow to $650 Million+ in annual revenue and 10,000+ employees, and we're continuing to invest in research and development in the form of Accelerators (prototype applications) developed in our global Financial Innovation Labs (FinLabs).

Responsibilities & Qualifications

  • Understanding Global Stock Loan business objectives and developing models that help to achieve them, along with metrics to track their progress.
  • Analyzing the ML algorithms (Regression, Classification, Ensemble, Neural Network) that could be used to solve a given problem and ranking them by their success probability.
  • Finding alternate and available datasets online that could be used for training
  • Exploring and visualizing data (with Tableau or Pandas and Matplotlib) to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world.
  • Defining the preprocessing and feature engineering to be done on a given dataset (correlation, missing-ness, imputation)
  • Defining data augmentation pipelines if needed.
  • Defining validation strategies (holdout, cross validation, walk forward, random sampling, bootstrapping)
  • Training models (Traditional and Deep learning for time-series data) and tuning their hyper-parameters.
  • Analyzing the errors (plot train and test/validation errors) of the model and designing strategies to overcome them.
  • Deploying models to production.
  • Programming experience in Python (sklearn, pandas, numpy, tensorflow), SQL.
  • Knowledge of the trade lifecycle (Trading, Middle Office, Settlements) a plus

Basic Qualifications

  • Advanced degree or equivalent in computer science, math, statistics or a related discipline
  • Critical thinking and ability to think about when to use which technology and why.
  • Ability to self- motivate, multi task and think independently
- provided by Dice

 
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