The Team:
Upstart’s Machine Learning team has a direct impact on our company's success. The team consists of Research Scientists, Data Scientists and other highly specialized roles who build and enable our core production models.
Because our challenges are so new, members of our Machine Learning team need strong creative problem-solving skills and the technical background to implement solutions. Our research environment affords team members the opportunity to utilize a variety of statistical and machine learning methods with the freedom and encouragement to pursue alternative approaches to solving problems. Whether developing new products or identifying novel approaches to core models, we are continuously seeking the next big ideas to move our business forward.
As a Data Scientist, you'll leverage technical and business acumen to become an expert in our models and how the models interact with the business. Identify potential problems or opportunities for improvement, communicate these issues to the team, stakeholders and suggest solutions. This involves self-directed investigation into our business and its data, designing and preparing regular reports, as well as conducting ad hoc analyses for the Machine Learning team, company leadership, and external business partners.
How you’ll make an impact
- By presenting reports to key stakeholders and decision makers on model performance and how it impacts the business. Oftentimes this person will report directly to corporate executives.
- By finding issues with the models before they have a chance to impact our business.
- By analyzing experimental data to uncover heterogeneous effects and recommend opportunities for personalized interventions.
- By collaborating with research scientists to build and evaluate novel models and modeling applications.
- By writing clear, bug-free, and well documented code that runs well and is easy to understand.
Minimum Qualifications
- MS degree or related experience in a production setting
- 5+years experience in data science projects
- Knowledge in applied statistical methods and causal inference (e.g., AB testing, hypothesis testing, linear and logistic regression)
- Experience with modern predictive modeling techniques (XGBoost, artificial neural networks)
- Experience with Python or R and SQL
- Strong Machine Learning experience
Preferred Qualifications
- PHD in Applied Stats, Mathematics, Economics
- A subject matter expert on model performance evaluation in a business context
- Experience in building visualizations and maintaining Dashboards (Plotly or Matplotlib) to track model performance
- Serving as an expert in the areas of experimental testing, Causal Inference, A/B testing or Hypothesis testing
- Collaborating with and mentoring other data scientists in these areas.
Position location This role is available in the following locations: Remote
Time zone requirements The team operates on the East/West coast time zones.
Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.
What you'll love:
- Competitive Compensation (base + bonus & equity)
- Comprehensive medical, dental, and vision coverage with Health Savings Account contributions from Upstart
- 401(k) with 100% company match up to $4,500 and immediate vesting and after-tax savings
- Employee Stock Purchase Plan (ESPP)
- Life and disability insurance
- Generous holiday, vacation, sick and safety leave
- Supportive parental, family care, and military leave programs
- Annual wellness, technology & ergonomic reimbursement programs
- Social activities including team events and onsites, all-company updates, employee resource groups (ERGs), and other interest groups such as book clubs, fitness, investing, and volunteering
- Catered lunches + snacks & drinks when working in offices
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