Senior Machine Learning Engineer

Company: Snap Finance
Location: Rochester, New York, United States
Type: Full-time
Posted: 20.MAY.2021

Summary

Are you looking for a friendly, fast-paced workplace with an emphasis on providing real, visible impact to a variety of stakeholders? Snap F...

Description

Are you looking for a friendly, fast-paced workplace with an emphasis on providing real, visible impact to a variety of stakeholders? Snap Finance is a thriving leader in the financial services industry, and our team members are the foundation of our success. Snap knows that happy, empowered, and engaged team members are essential to innovation and business success - and our approach is working. Come join us!

We are looking for machine learning engineers to join our machine learning team. The ideal person will have industry experience working on a range of classification and optimization problems. This person will take their expertise and apply it to exciting and diverse data with some of the most interesting and challenging inference problems in machine learning. Position would include model development and deployment and compiling/analyzing data from multiple sources with the objective of improving predictions to reduce risk, empower consumers and broaden our influence in a rapidly growing market of alternative finance.

The Job

  • Develop state-of-the-art and scalable models and tools leveraging machine learning, optimization, and rules-based models with an emphasis on optimizing credit risk and analyzing customer behavior
  • Propose, gather and integrate data and create valuable features
  • Code projects in parallel with the data engineering team
  • Assemble large, complex data sets that meet functional and non-functional business requirements

You

  • M.S in a quantitative field such as Statistics, Econometrics, Mathematics, Physics, Computer Science, or related quantitative field.
  • B.S in the fields described above will be considered if skill set and experience are robust
  • 3+ years experience in one or more of the following areas: machine learning, recommendation systems, data mining, or related research

Skills, Experience & Qualifications Required

  • Strong background in Python, Java , or other general-purpose programming languages
  • Experience with classification methods (e.g., Neural Networks, Gradient Boosting, Decision Trees, Random Forest)
  • Experience with filesystems, server architectures, and distributed systems
  • Sequence modeling (e.g.RNNs, Seq2Seq, ARIMAX)
  • Statistical analysis (e.g., Hypothesis testing, experimental design, hierarchical modeling, Bayesian and Frequentist methods)
  • Experience with automated workflows: Airflow, Jenkins, etc.
  • Experience with AWS cloud services such as EC2 and S3
  • Working knowledge of message queuing, stream processing, and highly scalable data stores
  • Familiarity with common computing environment (e.g. Linux, Shell Scripting)
  • Strong SQL skills
  • Proven ability to translate insights into business recommendations

Personal Characteristics:

  • Strong analytical and problem solving skills
  • Excellent communication skills with the ability to clearly explain technical topics to a non-technical audience
  • Team-oriented but able to complete tasks independently at a high standard
  • Structured, organized and detail-oriented
  • Proactive, enthusiastic and flexible
  • Fluency in English, both in oral and written form
  • Ability to take projects from conceptualization to implementation
  • Must be able to work onsite in our Salt Lake City office and be legal to work in the United States

What we offer:

  • Competitive salary
  • Health Benefits
  • Generous vacation
  • Great office culture
  • Ping-Pong and pool tables
  • Snacks and rocket fuel
  • Mac computer
  • Great growth potential in a rapidly changing company

More

Snap values diversity, and all qualified applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Learn more by visiting our website at

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