Data Scientist (w/m/d)

Key Skills

Machine learning techniques, MLOps tools, MySQL, Pandas, Postgres, Python Programming, R Programming, Snowflake, SQL

Job Description

As a Data Scientist, you will be part of the bigger decision science team, and will be contributing to one of our core decision areas - fraud domain, build experiments to test your hypothesis, perform in-depth analysis and investigation to understand fraud patterns, and get your hands on practical experience in ML by building and implementing fraud detection models across different portfolios.

This role will allow you to communicate your findings with the different stakeholders (Product, Engineering, and Business) and make an impact at Billie.

In This Role, You’ll Get to

• Apply your expertise in quantitative analysis, data mining, data science, and the presentation of analytics results to understand fraud, how it relates to Billie’s strategy and products, and how to prevent it
• Build advanced predictive models to understand the impact of specific fraudulent behaviors, trends, and patterns
• Work as a part of cross-functional teams of data and software engineers, analysts, product managers, operations, and business leaders to minimize impact of fraudulent behavior
• Communicate data-informed insights and recommendations to key stakeholders, discuss requirements with engineering and product partners for efficient implementation

What You’ll Need To Succeed

• 3+ years of experience in a data driven role
• Proficient in Python (pandas, scikit-learn, xgboost), R (tidyverse/tidymodels packages) and SQL (MySQL, Postgres, Snowflake)
• Experience with binary classification (boosted trees and similar models), explainable ML, graph networks analysis and anomaly detection
• Experience with DVC and similar MLOps tools (nice-to-have)
• Practical experience working across all stages of ML projects from conception to prod, either independently or under supervision of a senior
• Experience in detecting and analyzing fraud (IMO optional/nice-to-have)
• Experience managing and communicating to non-technical stakeholders

What we offer:

• Flexible work hours and trust in your ability to deliver, empowering you to take control of your work-life balance.
• Up to 6 weeks per year of “Workation”.
• One of the best Virtual Shares Incentive Programs in the market, so that everyone at Billie is invested in our success.
• Freedom to balance working from home and the office according to you and your team’s needs. No restrictions, only guidelines and a flexibility-first approach.
• Our “Catch a Ride with Billie” program gives you a big discount on the Monthly Public Transport pass in Berlin.
• A yearly development budget to broaden your skill set and horizons. We also offer free German group classes with your colleagues.
• An English-speaking, multicultural team with more than 46 nationalities from all continents represented in our staff.
• A one-time relocation bonus if you join us in Berlin.
• A great office space near Checkpoint Charlie with free gym access, healthy snacks and cereals, fruit, coffee, soda, and beer.

Billie offers you the opportunity to be a part of one of the fastest-growing Fintech startups in Europe following the mission to innovate to create new freedom for businesses of all sizes. Our combined decades of experience in B2B Financing and Payments in a market thirsty for innovation and change make this a fantastic possibility to get into the most dynamic space in tech.

Join an international team of talented, passionate people where drive and merit matter. We work in nimble, cross-functional teams with open communication lines across the company. You’ll be surrounded by smart people from a wide variety of backgrounds from which you can learn and that want to learn from you.

Are you ready to join Billie?

Billie is proud to be an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment in our workplace. By embracing talents and abilities of all kinds, we aim to boost motivation and team creativity. We do not discriminate on the basis of race, religion, national origin, age, marital status, gender, political views, beliefs, sexual orientation, color, disability status, or any other demographic factors.