Opportunity at
UpstartApplied Scientist
Upstart, a lending marketplace that works with banks and credit unions, is seeking an Applied Scientist for its Machine Learning Growth team. That team builds models…
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About This Role
Upstart, a lending marketplace that works with banks and credit unions, is seeking an Applied Scientist for its Machine Learning Growth team. That team builds models that determine which prospects to pursue across marketing channels. Its Direct Mail sub-team applies causal machine learning to forecast incremental conversions and rank prospects, and that scope is now extending to home equity lines of credit, email, digital and other channels. The scientist would…
Job Description
Upstart, a lending marketplace that works with banks and credit unions, is seeking an Applied Scientist for its Machine Learning Growth team. That team builds models that determine which prospects to pursue across marketing channels. Its Direct Mail sub-team applies causal machine learning to forecast incremental conversions and rank prospects, and that scope is now extending to home equity lines of credit, email, digital and other channels.
The scientist would strengthen current models and develop new methods, reviewing past model and campaign outcomes to find improvements. Work includes investigating features and architectures, designing careful experiments that isolate causal effects, and creating reusable pipelines and metrics from untidy data. The person collaborates with ML, Growth and marketing platform engineers to turn business questions into research agendas and production systems, and helps bring models into lifecycle, email and digital marketing.
Required background includes a master's degree in a quantitative field such as statistics, economics or operations research, practical use of statistical and machine learning methods, and Python for analysis and modeling. Causal inference and experiment design experience is also required. A doctorate, familiarity with causal machine learning and marketing or acquisition analytics are preferred. The position is remote across the United States and Canada, excluding Quebec, and includes in-person sessions that typically last two to four days, one to two times per quarter.
Responsibilities
- Study past model and campaign results to find ways to improve effectiveness
- Build and assess machine learning models, including new features and architectures
- Design rigorous experiments and evaluations that measure causal lift
- Develop reusable pipelines, metrics and analytical methods from complex data
- Collaborate with ML, Growth and marketing platform engineers to deliver production solutions
- Extend models to lifecycle, email, digital and other new channels
Requirements
- Master's degree in a quantitative discipline such as statistics, economics or operations research
- Practical use of statistical and machine learning methods for modeling or data science
- Python for data preparation, analysis and model development
- Experience in causal inference and experimental design, with rigorous model evaluation
Qualifications
- PhD or equivalent
- Understanding of causal ML techniques
- Skill in translating broad business goals into structured research questions
- Experience moving from exploration through experiments to production scaling
- Work applying ML to marketing, acquisition, lifecycle or growth problems
Core Skills
Benefits
- Pay package with bonus potential and annual equity that vests quarterly
- Retirement savings with employer matching of $2 for each $1, up to $15,000 a year (USD in US, CAD in Canada)
- Employee share purchase plan for eligible US staff
- Medical, dental, vision and wellness coverage, plus supplemental health coverage in Canada
- Health savings account contributions for eligible US plans
- Life and disability insurance, and paid vacation, sick days and holidays
- Paid family leave and fertility, parenthood and caregiving support
- Employee assistance program and annual wellness and productivity allowances
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Where is the job located?
The posting lists the role as remote in the United States or Canada, excluding Quebec, with periodic in-person onsites. Office locations include Columbus, Austin, the Bay Area and New York City.
What is the salary?
The posted US base range is USD 141,500 to 196,000. A Canada range of CAD 130,900 to 160,000 is also listed, and pay varies by location.
What degree is required?
A master's degree in a quantitative field, plus experience with causal inference and Python.
Is the job full-time?
The listing is for a full-time position.
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