Data Scientist (m/f) in Berlin, Germany // Blackbill
The vision is to become the leading European tech company to provide innovative credit products to small businesses and freelancers by pre financing open invoices that get paid in 1 to 3 months. We have developed a fully automated algorithm and processes together with a partner bank and are currently raising a large financing round to grow internationally throughout Europe. Exciting times ahead, you can be part of the journey and develop in any direction that you want in a fast-paced, high learning, fun environment.
Current team size is 36 employees, our office is located right next to the Spree opposite of Watergate club.
We’re off to something big and just got started – join us now and be part of the ride.
- You will advocate, evangelize and build data-fuelled products that help our customers receive most competitive and fair credit/factoring offer on the market. You’ll dig in and become an expert on our finance datasets. You will provide insight into leading analytic practices, design and lead iterative learning and development cycles, and ultimately produce new and creative analytic solutions that will become part of our core deliverables.
- You will work with cross-functional team members to identify and prioritize actionable, high-impact insights across a variety of core business areas. You will lead applied analytics initiatives that are leveraged across the breadth of our solutions for Fintech Industry. You will research, design, implement and validate cutting-edge algorithms to analyze diverse sources of data to achieve targeted outcomes.
- As our data scientist, you will provide expertise on mathematical concepts for the broader applied analytics team and inspire the adoption of advanced analytics and data science across the entire breadth of our organization.
- And most importantly, thanks to your great team player and mentorship skills, you will be a go to person for the company in case of any data/scoring related problem.
- You have a Ph.D. or Master’s Degree in operations research, applied statistics, data mining, machine learning, physics or a related quantitative discipline. You have a deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimization algorithms.
- With 4+ years of experience delivering world-class data science outcomes, you solve complex analytical problems using quantitative approaches with your unique blend of analytical, mathematical and technical skills.
- You’re passionate about asking and answering questions in large datasets, and you are able to communicate that passion to product managers and engineers. You have a keen desire to solve business problems, and live to find patterns and insights within structured and unstructured data. You propose analytics strategies and solutions that challenge and expand the thinking of everyone around you.
- You are expert in analyzing large, complex, multi-dimensional datasets with a variety of tools. You are accomplished in the use of statistical analysis environments such as R, MATLAB, SPSS or SAS. You have experience with BI tools such as Tableau and Microstrategy. You’re as comfortable with relational databases as you are with Hadoop-based data mining frameworks. You are familiar with SQL, Python, Java.
- You desire a fast paced, test-driven, collaborative and iterative engineering environment. You love learning, data, scale and agility. You excel at making complex concepts simple and easy to understand by those around you. You’re driven to show the world the power of applied analytics.
What You Get:
- Full exposure to our methodology and ecosystem, highly data driven technology
- A unique fintech startup that just got a 7-digit funding by top VC firms
- A management that is dedicated to your personal development and a dynamic team that you’ll have fun with
- An outrageous office in the vibrant capital of Germany, Berlin
- A NERF gun
- Office parties on our amazing balconies right next to the Spree (it’s a river)
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