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Data Scientist

Atlanta, GA, US /...
R

Reframe (Glucobit)

Startup
Category
Technical
Experience
0-3 years
Type
Full-Time
Location
Atlanta, GA, US / Remote (US)
Salary
$142,000 - $200,000
Job Description
Benefits & Culture
Flexible hours
Remote work
Paid time off
Fast-paced environment
Direct feedback
Growth opportunities
This goes straight to the founder
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Founder Signals
Response Rate
85%
Avg Response Time
2 hours
Compensation Calculator
In startups, higher salary usually means lower equity.
Salary (affects equity %)
Link salary ↔ equity
$125,000
$142,000$200,000
Equity % (auto from trade-off model)
1.19%
0%5%
Projected Exit Value
$350,000,000
$10M$500M
Live Trade-off
Salary
Equity %
Est. Equity Value at Exit
$100,000
1.19%
$2,082,500
$125,000
1.19%
$2,082,500
$150,000
1.10%
$1,918,362
$175,000
0.80%
$1,405,431

Advanced Salary & Equity Calculator

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About Reframe (Glucobit)

Reframe is the future of digital behavior modification. Our mission is to help people break negative habit loops and unlock the best version of themselves - starting with Alcohol and we're growing crazy fast! In the last 6 months we've grown 3000%, become one of the top health apps on the app store, and are currently helping 100K+ customers cut back on Alcohol use. We are now looking to help the next billion people Reframe the way they think and drink!

About the role

Reframe is an app to help people cut back on drinking alcohol and discover the best version of themselves. We're currently the market leaders in the alcohol reduction space with over 150K paying customers & growing fast (we 10X'ed in 12 months to over $13M in Annual Revenue). Our mission is to help people achieve what we call peak human condition (best version of themselves mentally, physically, emotionally) by helping people build great habits and manage their emotions (which is also how we help people cut back on drinking) through an end-to-end lifestyle management system.

Job Description: As a Data Scientist, your primary responsibilities will include:

  1. Data Collection and Management:

    • Collaborate with the engineering team to establish robust data collection pipelines, ensuring the integrity, security, and accessibility of user data.
    • Design and implement data warehousing and storage solutions to efficiently manage the growing volume of user data.
    • Develop data quality assurance processes to maintain the accuracy and reliability of the data.

  2. Data Cleaning and Preprocessing:

    • Implement robust data cleaning and preprocessing techniques to address issues such as missing values, outliers, and data inconsistencies.
    • Develop automated data transformation and normalization pipelines to ensure the data is in a format suitable for analysis.
    • Work closely with the engineering team to streamline the data cleaning process and maintain the integrity of the data.

  3. Exploratory Data Analysis:

    • Conduct in-depth analyses of user behavior, engagement, and outcomes to uncover patterns, trends, and opportunities for optimization.
    • Leverage statistical techniques and machine learning models to identify the key factors influencing user success in reducing alcohol consumption.
    • Generate insightful visualizations and dashboards to communicate findings to cross-functional stakeholders.

  4. Predictive Modeling and Personalization:

    • Develop predictive models to anticipate user needs, behaviors, and potential risk factors related to alcohol consumption.
    • Implement personalized recommendation algorithms to deliver tailored interventions, content, and features to each user based on their unique characteristics and progress.
    • Continuously refine and iterate on the predictive models to improve the accuracy and effectiveness of the personalized user experience.

  5. Experimentation and A/B Testing:

    • Design and execute rigorous A/B tests to evaluate the impact of new features, interventions, and user experience enhancements.
    • Analyze the results of these experiments to make data-driven decisions and guide the product roadmap.
    • Collaborate with the product and engineering teams to translate insights into actionable improvements.

  6. Cross-Functional Collaboration:

    • Work closely with the product, engineering and user experience teams to align on data-driven priorities and ensure the seamless integration of data-centric solutions.
    • Communicate complex data insights and recommendations to non-technical stakeholders in a clear and compelling manner.
    • Stay up-to-date with the latest trends and best practices in data science, machine learning, and user behavior analytics.

Qualifications:

  • Bachelor's or Master's degree in a quantitative field, such as Computer Science, Statistics, Mathematics, or Data Science.
  • Proven experience in data collection, cleaning, and preprocessing using tools like SQL, Python, or R.
  • Expertise in statistical analysis, machine learning, and predictive modeling techniques.
  • Strong problem-solving skills and the ability to translate data insights into actionable recommendations.
  • Excellent communication and presentation skills, with the ability to effectively collaborate with cross-functional teams.
  • Experience in the health or wellness industry, particularly in the context of addiction or behavior change, is preferred.

Technology

We primarily help people through our IOS (Native - Swift) & Web Apps. We are looking for talented engineers, developers, and designers to help us combine Behavioral Neuroscience, AI and principles of game design to create a truly immersive habit modification experience.

Requirements:

  • Experience working with small, lean engineering teams to ship product fast
    • Experience working on real-time applications & games/social products is a big plus
    • Love working with ex-founders or early engineers from fast-growth teams
  • Experience with Swift, Node, React, and Js libraries (or other comparable web frameworks)
  • A history of striking out on your own & building
  • Interest in human psychology & an eye for good design

Interview Process

  1. Initial screening with our Ops team
  2. Interview with CEO
  3. Interview with Head Product Engineer
  4. Take Home Task
  5. Final Interview with Head Software Engineer