Solution Architect

Ovative  |  www.ovative.com

Published: May 9, 2022

Location: Minneapolis, MN

Category: Data Engineering, Science, and Visualization

Job Type: Full Time



Description

About Ovative Group

Ovative Group is the premier independent media and measurement firm in the United States. We help change makers, in fast-growing, customer-centric organizations across industries reinvent their marketing and measurement programs. We leverage our media, measurement, and consulting capabilities to help brands like Coach, Kate Spade, Stuart Weitzman, Facebook, The Home Depot, CVS, Disney, and UnitedHealth Group transform their media and marketing programs. Our proprietary approach to measuring and optimizing marketing investment decisions, Enterprise Marketing Return (EMR), is disrupting the industry and setting the gold standard for customer and marketing strategy, activation, and measurement. 

Digital Marketing is a critical part of every consumer journey, but many companies only measure its impact on their digital outcomes. Our focus on enterprise-level optimization at Ovative Group can't be found anywhere else, and our clients are household names that span multiple industries, including retail, healthcare, education, CPG, and hospitality.

Recognized six consecutive years on Star Tribune’s list of Top 150 Workplaces and four years on Inc.5000’s list of the fastest growing private companies in America, we pride ourselves in always overdelivering for our clients, our teams, and our communities. 

About the Role

The Machine Learning Engineer -- Platform Engineering position is an opportunity to build robust, scalable, efficient, and modular machine learning tools for our flagship enterprise marketing measurement product, MAP. You will own our production ML pipelines from data ingestion to reporting. While partnering closely with R&D Data Scientists, Data Engineers, Data Visualization Engineers, and the Product team you will increase the scope and reach of Ovative Group's foundational technology.

What You'll Be Doing:

  • Build infrastructure, tooling, and processes to deploy ML solutions (70%)
    • Utilize modern software and ML engineering principles and tools to enable training, deployment, monitoring, and lifecycle management of ML models for our best-in-class measurement and attribution product
    • Partner with internal Data Science and Data Engineering teams to move their MVPs into production
    • Increase the efficiency, extensibility, and modularity of our current solutions for expansion into new clients and market verticals
    • Develop new APIs/Microservices
    • Establish yourself as an internal MLOps subject matter expert
  • Execute and QA client-specific model pipelines and communicate results (30%)
    • Onboard new clients to MAP in partnership with other internal data teams
    • Update, refresh, and QC client-specific models

Requirements

  • Solid understanding of software engineering concepts (like OOP & design patterns)
  • Strong Python (or similar languages) development experience outside of notebooks
  • Enjoy the ambiguity of building data products and are comfortable balancing perfection vs. good enough
  • An ML builder mindset -- more interested in ML scaling and robustness than ML modeling
  • Experience developing and deploying MLOps solutions
  • Developed new APIs/Microservices
  • Tackled problems relating to performance, scalability, and resilience
  • Experience with production cloud environments (we use GCP)
  • Collaborative mindset, strong interpersonal skills, and the ability to translate between technical and non-technical audiences
  • Understanding of statistics, machine learning, and hypothesis testing
  • A few years of experience in a data science, data engineering, or machine learning engineering role

Preferred:

  • Built and productionized applied machine learning products
  • Hands-on time with Airflow, Kubernetes, Docker
  • Experience with marketing attribution algorithms, like MMM or MTA
  • Exposure to web development and frameworks
  • Marketing/Martech/Adtech domain experience

Pay Transparency

At Ovative, we offer a transparent view into three core components of your total compensation package: Base Salary, Annual Bonus, and Benefits. The salary range for this position below is inclusive of an annual bonus. Actual offers are made with consideration for relevant experience and anticipated impact. Additional benefits information is provided below.

For our Machine Learning Engineer positions, our compensation ranges from $62,000 to $93,000, which is inclusive of a 15% bonus.

Benefits of Working at Ovative Group

We provide strong, competitive, holistic benefits that understand the importance of your life inside and our of work

Culture:

Culture matters and we've been recognized as a Top Workplace five-years running because of it. We demand trust and transparency from each other. We believe in doing the hard and complicated work others put off. We're open in communication and floor plan. We're flat- our interns sit next to VPs, our analysts work closely with senior leaders, and our CEO interacts with every single person daily. Put together, these elements help foster an environment where smart people support each other in performing to their highest potential.

Compensation and Insurance:

We strive to hire and retain the best talent in the Twin Cities. Paying fair, competitive compensation, with a large bonus incentive, and phenomenal health insurance is an important part of this mix. We're rewarded fairly and when the company performs well, we all benefit.

Tangible Amenities We Enjoy:

  • Flexible paid vacation policy
  • 401k match program
  • Free parking and on-site fitness center with locker rooms
  • Sabbatical program
  • Charitable giving via our time and a financial match program
  • Shenanigan's Day

Working at Ovative won't be easy, but if you like getting your hands dirty, driving results, and being surrounded by the best talent, it'll be the most rewarding job you'll ever have. If you think you can make us better, we want to hear from you!

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