Staff Applied Scientist

Key Skills

A/B Testing, Analytical and Problem solving, Docker, KPI, Python Programming

Job Description

Investing in real estate is one of the most reliable and powerful ways to build generational wealth. From Main Street to Wall Street, investors remain bullish on the long-term returns from residential real estate, especially the single-family home. More Americans are choosing to rent single-family homes for the convenience and low cost of entry. And a new breed of investors is opting to skip the starter home and buy investment properties instead, with a catch: they want to buy outside their own backyard, in fast-growing, mid-sized cities where the price is right and the returns are high.

But investing in real estate isn’t easy - and doing it remotely is even more challenging. From finding the right assets in the right neighborhoods for the right price, to renovating, leasing and managing the property, to collecting rent and handling repairs, to ensuring you’re staying compliant with local laws and regulations, managing a remote investment portfolio can become a full-time job.

That’s where Mynd comes in. We’re a tech-enabled property management and real estate investment company serving more than 18,000 homes across 26 markets. We help individual and institutional investors buy, renovate, market, manage and sell single-family homes. And we help our residents find safe, spacious, and low-maintenance homes from which to launch their lives.

We bring together talent and expertise from real estate and tech to build a proprietary platform that is unmatched in our industry. Powerful workflows, AI tools and intuitive consumer apps help our teams deliver great service, make life easy for residents, and give investors powerful portfolio insights at their fingertips.

We’re making it possible for a new generation of Americans to dream their way - decoupling the homes they live in from the homes they invest in.

We’re grateful to have been named one of Fortune's “Best Places to Work” 2023, a Built-In SF Best Places to Work winner, the #1 fastest-growing company in the East Bay by the San Francisco Business Times, one of the 26 hottest proptech startups of 2023 by Business Insider, and one of America’s Best Startup Employers of 2023 per Forbes. We’ve attracted A players from Starwood Waypoint Homes, Opendoor, McKinsey, BCG, Amazon, Facebook, Upwork, WeWork, One Medical, and Zillow. We’re backed by top VCs, including Lightspeed, Canaan, and Jackson Square, and major institutional investors including Invesco Real Estate.

Join us!

About the role:

As an Applied Scientist at Mynd, you will be part of a high-performing team building predictive models for a full-stack real estate business. You will leverage machine learning and data engineering tools, and apply operations research and statistical methods to solve complex challenges like vacancy rates estimation, rent predictions, price valuations, customer churn, routing problems etc. that have tangible impact on the company’s growth and bottom line. You will be a key contributor to Mynd’s machine learning strategy and help design systems that support natural language processing, computer vision, and generative AI.

This high visibility role offers the opportunity to collaborate closely with cross-functional business partners and present solutions to broad audiences as you drive strategic business decision making and help build the company’s infrastructure and tech stack.

Responsibilities: 

• Develop deep understanding of customer problems and operational metrics
• Partner with stakeholders to turn vague issues into well-defined and measurable optimization problems
• Lead the design and implementation of end-to-end machine learning projects that have a high degree of ambiguity and complexity
• Create and maintain data pipelines for data collection, feature engineering, and model training
• Work closely with engineering teams to integrate machine learning models into production applications, ensuring scalability, stability, and maintainability
• Design and implement data pipelines and KPIs to track model accuracy and usage
• Improve model performance through integration of new datasets and A/B testing
• Provide technical leadership and contribute to the growth and development of the data science team

Preferred Qualifications:

• Masters/Ph.D. degree in a quantitative field (mathematics, statistics, finance, physics, etc.)
• 6+ years of experience as a data scientist working on machine learning models in big data environments
• Mastery in a scripting language (Python) and a database querying language (SQL)
• Strong programming and data engineering skills
• Experience implementing machine learning models and optimization algorithms on large data sets
• Strong intellectual curiosity and critical-thinking skills
• Solid written and verbal communication skill

Any offer of employment is conditioned upon the successful completion of a background investigation.

Compensation:

$163,000 - $220,000 annually. The compensation range may be adjusted based on experience and location.

At Mynd, we offer a robust, competitive & unique benefits package:

• Unlimited time off
• 13 Paid holidays
• Paid Parental Leave
• Cash to purchase your own investment property through our “Project Investor Myndset” program
• 401k + Match
• Wellness, home office, and cell phone subsidies
• Volunteer time off
• Robust health, dental, vision insurance, and more
• Sabbatical program
• Eligible to participate in Mynd’s equity program

At Mynd, we expect our team members to live and work by our values:

• Be Myndful
• Earn trust
• Team first
• Be an A player
• Think like an investor

As part of our dedication to diversity, Mynd is an Equal Opportunity Employer. Individuals seeking employment at Mynd are considered without regard to race, ethnicity, color, age, sex, religion, national origin, ancestry, pregnancy, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, registered domestic partner status, caregiver status, marital status, veteran or military status, citizenship status, or any other legally protected category.