Lead Machine Learning Engineer
P3785
Posted on 2/13/2023
INACTIVE
84.51 Degrees

1,001-5,000 employees

Retail data science company enhancing customer experiences
Company Overview
84.51° stands out as a leading retail data science company, utilizing advanced data science and predictive analytics to create personalized experiences for shoppers, thereby providing a competitive edge to its clients. The company's unique strength lies in its access to first-party retail data from nearly half of US households and over 2 billion transactions, which enables a more customer-centric approach. With its specialized services like 84.51° Insights, 84.51° Loyalty Marketing, and Kroger Precision Marketing, 84.51° helps brands effectively engage customers at every point of their purchasing journey.
Consumer Goods
Data & Analytics

Company Stage

N/A

Total Funding

$5.5M

Founded

2015

Headquarters

Cincinnati, Ohio

Growth & Insights
Headcount

6 month growth

1%

1 year growth

7%

2 year growth

24%
Locations
Northbrook, IL, USA • Remote • Chicago, IL, USA • Portland, OR...
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Microsoft Azure
Python
Data Science
Tensorflow
Management
Data Structures & Algorithms
Pytorch
Apache Spark
Docker
REST APIs
Linux/Unix
Data Analysis
CategoriesNew
AI & Machine Learning
Software Engineering
Requirements
  • MS or PhD in Machine Learning, Computer Science, Computer Engineering, Applied Statistics, or related field
  • 2-4 years of experience using Deep Learning frameworks such as Tensorflow, Pytorch, Fast.ai, Mxnet or HuggingFace
  • 1-2 years of experience with Embeddings, Recommender Systems
  • Hands-on experience with distributed data processing technologies such as Spark and ability to build data pipelines in cloud (eg. Azure)
  • Knowledge of approximate & large scale algorithms ( e.g sketches, hyperloglog), efficient algorithms for processing large scale datasets (e.g map-reduce) is required
  • Hands-on experience developing software tools that scale (i.e. Python packages) and using end-to-end tooling to develop, test, and deploy these tools (i.e. CI/CD)
  • High level of independence, able to make time-sensitive decisions rapidly and solve urgent problems without escalation
  • Excellent communication skills, particularly on technical topics. Must be able to learn from others and teach others, and to work collaboratively as part of a highly interdependent team
  • Comfort with independent learning of new technologies, and willingness to jump into using unfamiliar tools
  • Other data science-adjacent technology experience would be beneficial but is not required, including Docker, Rest APIs, Fast API, Linux and basic shell scripting
  • Strong time and project management skills; the ability to balance multiple simultaneous work items and prioritize as necessary
Responsibilities
  • Provide technical leadership across a variety of technologies, with a focus on Python, Spark, machine learning tool development, and software engineering best practices for scaling machine learning solutions
  • Stay up to date on emerging trends in the data science world and pioneer the use of new tools in the data science function
  • Research state of the art machine learning algorithms, patterns, processes, and tooling to identify new opportunities for implementation across the enterprise
  • Developing tools and patterns to implement machine learning and science solutions
  • Collaborate with teams across the enterprise to implement and standardize science solutions as internal packages, tools, or patterns throughout the company
  • Optimize machine learning processes for performance, accuracy, and software engineering best practices
  • Build production-grade solutions to scale, manage, and serve machine learning models and science solutions
  • Educate stakeholders on machine learning and advanced programming topics as needed, through both formal instruction and informal partnership
  • Communicate important technical information clearly to upper management to steer organizational direction, to teammates as part of project work, to other data scientists to guide their work, and to less technical functions such as product management
  • Partner with a wide range of technical personas (i.e., engineering, architecture, data scientists…) to identify and implement best practices around software engineering and analytic procedures