Flywheel Software is a high-growth technology startup offering a customer segmentation platform on Snowflake and BigQuery that is changing the way businesses acquire, retain, and win-back their customers. We are a completely bootstrapped and profitable startup with clients like Indeed, Google, and Uber.
Our Mission 🧭
Unlock the value of Cloud Data to make it drive massive, real world impact.
How we work 💼
- Collaborative team that strongly believes in taking the learner's mindset
- We encourage exploration of new technologies
- We believe in empowering every person on our team to do their best work
- Dedication to building a product people love
- We build slowly but surely for the long term. We are transparent about the challenges of building a great company. We are humble in facing those challenges. But, we know that if we keep improving every day the Flywheel turns.
About the Position
We are looking for an outstanding Data Science Engineer that wants to learn how to combine the best of strategy and machine learning to product outsized business impact. This role will have the opportunity to learn how to apply machine learning / artificial intelligence models to some of the most important problems businesses face.
● Query massive datasets efficiently to build data models
● Visualize data in useful, clear and interesting ways
● Build machine learning models on billions of data points
● Develop presentations articulating Flywheel’s results to clients
● Build out customer marketing strategies to retain and upsell customers through marketing programs via email, retargeting and mobile interactions.
What You Will Be Doing: A Sample Day in the Life of a ADSE @ Flywheel Software
8:30-9am, Planning: Plan out the day and post your top 3 goals to our daily-standup channel on Slack. Coordinate with team members where needed.
9-11am Deep Work: Prototype a minimum viable logistic regression model in a notebook to predict whether a particular segment of customers is likely to request a refund for their purchase in the next 14 days based on features like prior returns, use of offer codes and whether they purchased on mobile or desktop.
11am-11:30am Internal Team Meeting: Review a Tech Spec presented by the Software Engineering team on how we could generalize a segment performance evaluation metric on an automated basis. Discuss specific client instances that would help inform the engineering team’s design.
11:30-12pm Client Meeting #1: Present the findings you prepared yesterday for a segmentation model that incorporates purchase recency, frequency, and basket size. Walk through data distributions, correlations and other important visualizations to build consensus with the client on the segmentation thresholds.
12-12:30pm Lunch & Learn: Watch a team member teach the basics of Kubernetes and how it supports our tech stack at various levels while you munch on lunch.
12:30-2pm Segmentation Modeling: Follow up on the feedback received from Client Meeting #1 to apply the new segmentation thresholds. Submit for code review to another team member.
2-2:30pm Debugging Session: Pair programming with a teammate to diagnose and fix a data pipeline issue. Share the results of your findings with the broader team and add alerts to catch this error preventatively in the future.
2:30-3pm Performance Analysis: Analyze the results of the existing segments running across Marketo and Google AdWords. Collect notes to present later at a client meeting.
3-3:30pm Client Meeting #2: Review the performance of existing segments with a client for statistically significant results. Make suggestions for increasing media spend for successful segments or otherwise pivot that segment to a new channel like email for a second experiment.
3:30-4pm Pushing to Production: Incorporate feedback on the code you submitted for review at 2pm. Ping the team member that reviewed your code for a quick clarification, and then resubmit for final sign-off before pushing code to production.
4-5pm Presentation Development: Incorporate the results of the regression model this morning into a few slides to review insights and findings before proceeding with further refining feature engineering and algorithm selection.
5-5:30pm Messages: Answer any open questions on Slack or Email from clients and other team members before signing off for the day.
- You are passionate about marketing data analysis and helping customers reach and exceed their goals.
- You have an insatiable intellectual curiosity
- You have Intermediate level Python experience with version control
- You have at least 4+ years’ work experience in Analytics or equivalent, including strong SQL skills (in addition to any internships).
- You have a demonstrated interest in machine learning.
- You have minimum of a Bachelors’ degree or equivalent.
- You have strong data visualization experience (e.g. Tableau, Looker, Google Data Studio)
- You have experience presenting to external clients.
- You are able to articulate clearly and concisely to a variety of audiences.
- You are able to self-manage (we don’t micromanage here)
What Makes You Standout
- You have completed a Bootcamp program in Data Marketing Analytics.
- You have strategy consulting experience.
- You have previous start-up experience.
- You have a Master’s Degree in Machine Learning, Advanced Mathematics or similar degree.
We are a collaborative team that strongly believes in taking the learner's mindset to everything we do. This role will have the opportunity to learn how to apply machine learning / artificial intelligence models to some of the most important problems businesses face.
By joining our team, we hope you will change the trajectory of your professional career and that of our business.
- Spot bonuses for major milestones
- Grow into leadership roles as we scale this rocket-ship
- Startup equity for star performers
Generous Time-off 🏝
- Take as much vacation as you like!
- Flexible remote work policies
Platinum Benefits 🧑🏻💻
- Free Platinum Health Insurance with Aetna
- 401(k) Program with Generous Company Match
Learn and Grow ✍️
- Education Stipend towards your professional development
- Work directly with Founders (ex-Googlers)
- Learners’ mindset culture of ‘Friendly Geniuses’