Data Scientist (North America)

Posted 3 Days Ago
Hiring Remotely in Austin, TX
Remote
1-3 Years Experience
Artificial Intelligence • Cloud • Machine Learning • Software • Database
The Role
ML Solutions Architect responsible for technical liaison with customers on the East Coast, showcasing how the Kumo platform can address their machine learning needs. Craft solution architecture, conduct product demos, guide customers, and maintain relationships with technical champions. Provide market feedback to refine product features and generate educational materials for users.
Summary Generated by Built In

Come and change the world of AI with the Kumo team!


Companies spend millions of dollars to store terabytes of data in data lakehouses, but only leverage a fraction of it for predictive tasks. This is because traditional machine learning is slow and time consuming, taking months to perform feature engineering, build training pipelines, and achieve acceptable performance.


At Kumo, we are building a machine learning platform for data lakehouses, enabling data scientists to train powerful Graph Neural Net models directly on their relational data, with only a few lines of declarative syntax known as Predictive Query Language. The Kumo platform enables users to build models a dozen times faster, and achieve better model accuracy than traditional approaches.


As one of the Data Scientist for North America, you will serve as a technical liaison for Kumo’s customers and prospects in the Americas. Your objective is to discover the technical needs of customers and showcase how Kumo can address them (or explain why you think it won’t).


With this information, you will craft and tell a story of how Kumo can deliver value to their organization.Together with the customer and Account Manager, you will put together a plan to solve the customer’s machine learning problems using Kumo.


You will leverage your industry knowledge and data science expertise to help the customer craft the solution architecture and machine learning approach for their use cases, and guide them to achieve technical wins. You will maintain relationships with technical champions, ensuring continued success of existing models as well as expansion to new use cases.

The Value You Will Add:

  • Be a Kumo platform superuser - understand the product in and out and how it should be used to solve customer problems.
  • Lead the technical discovery to understand the alignment between what Kumo offers and prospective customer expectations.
  • Conduct product demos of Kumo solving ML problems in a variety of verticals, including finance/fraud, growth/marketing, personalization/commerce, and forecasting/optimization.
  • Guide the customer to achieve meaningful wins on high-impact ML problems, by leveraging your problem-solving skills, data science knowledge, and industry experience.
  • Be hands-on, to help customers overcome challenges they may encounter in achieving sufficient model performance, or integrating Kumo into their production systems.
  • Lead architecture reviews and security assessments.
  • Maintain meaningful relationships with technical influencers and champions within ML teams, both pre and post-sale.
  • Educate current Kumo users on how to successfully use our product, best practices, etc. so that they increase usage across a larger and larger number of internal workloads.
  • Provide market and customer feedback to the Product and Engineering team to refine feature specifications and the product roadmap.
  • Create broader processes for each customer to go through to ensure we can drive repeatable successes in PoCs.
  • Generate Kumo platform educational materials to disseminate amongst current users or prospects.

Your Foundation:

  • Someone who finds genuine satisfaction in solving customer ML problems and helping them deliver value to the business.
  • 2+ years of relevant professional experience working with external customers in deploying AI/ML/data science solutions in production for customers.
  • Proficiency with ML and data science fundamentals, at the level of a bachelors/graduate program.
  • Persuasive communication – ability to present, speak, demo well to customer stakeholders and convince them to partner with Kumo!
  • Self-starter, motivative, resourceful and persistent: demonstrated ability to structure complex problems, take the initiative, and identify creative solutions to deliver outcomes in the face of obstacles.
  • Knowledge of common data science tools around SQL-based data warehousing (eg. Snowflake, Databricks, DBT), BI tools (eg. Tableau, Looker), workflow orchestration, and ML Ops.
  • Excellent spoken and written English skills.
  • Fluency with scripting in Python.
  • Ability to work effectively across time zones. Teammates will be located from PT to CET time zones. Customers will usually range from Pacific to Brasilia time.

-------------------------

Benefits: 

Stock

Competitive Salaries 

Medical Insurance

Dental Insurance 


We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Top Skills

Python
The Company
HQ: Mountain View, CA
38 Employees
On-site Workplace
Year Founded: 2021

What We Do

Democratizing AI on the Modern Data Stack!

The team behind PyG (PyG.org) is working on a turn-key solution for AI over large scale data warehouses. We believe the future of ML is a seamless integration between modern cloud data warehouses and AI algorithms. Our ML infrastructure massively simplifies the training and deployment of ML models on complex data.

With over 40,000 monthly downloads and nearly 13,000 Github stars, PyG is the ultimate platform for training and development of Graph Neural Network (GNN) architectures. GNNs -- one of the hottest areas of machine learning now -- are a class of deep learning models that generalize Transformer and CNN architectures and enable us to apply the power of deep learning to complex data. GNNs are unique in a sense that they can be applied to data of different shapes and modalities.

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