Data Scientist

at  Vanilla Steel

We are a Berlin-based startup that has successfully established a leading B2B marketplace for industrial metal trading across Europe.
The multi-billion-euro metal trading industry is operated on Excel, PDF and Email. We are on a mission to transform buying and selling in one of the oldest industries of the modern world with seamless and intuitive digital solutions. Our technologies increase liquidity, accelerate transactions, reduce scrapping rates and enhance buying convenience for hundreds of steel and metal distributors across Europe.
The Opportunity
Join our VC-backed company during this exciting phase of growth. We are creating a Data Team to oversee data analysis, data engineering and data science. As a Data Scientist, your primary contribution will be to develop our material matchmaking algorithm from zero to one, enabling spotting of new transactions and realizing direct impact on new transaction growth.
This position is based full-time in our vibrant Berlin office. We believe that working together in a dynamic office setting fosters collaboration and accelerates the growth of our ambitious startup.

Tasks

• Work closely with Product to understand customer/business needs, success metrics and priorities to plan out your own solution space and roadmap.
• Develop material recommendation algorithms based on live material inventory, purchase behavior and buying preferences.
• Develop models and algorithms to aggregate, clean, structure and standardize supplier inventory data from various data sources and formats including Excel, PDF, ERP and unstructured natural language.
• Design and analyze large-scale A/B tests to continuously improve and iterate algorithm performance.
• Evaluate algorithm performance to identify improvement opportunities.
• Proactively communicate, share your conclusions, and explain complex topics tailored to diverse audiences.
• Push new ideas into the design and concept funnel while keeping business priorities, trade-offs, and technical feasibility in mind.
• Create, develop, and maintain metrics on dashboards to measure algorithm performance and the value generated for our customers and for the company.

Requirements

• At least 2 years of experience working in data science or machine learning or statistical modeling.
• Experience working with data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. Matlab).
• Experience working with Product and Business stakeholders in order to understand the customer problems and goals for which you would be building out data centric solutions.
• Ability to communicate and work well with Technology teams to take the solution to the actual end user.
• An exceptional understanding of data analysis tools, techniques and limitations, statistics concepts, and A/B testing.
• Experience applying theoretical models in an applied environment.
• A degree in Computer Science, Mathematics, Statistics or another quantitative discipline.
• Excellent communication skills (both verbal and written) and confidence in presenting ideas and findings to stakeholders with the right level of detail.
• Experience with using solutioning approaches like Opportunity Solution Tree or something similar in order to independently identify top opportunities is nice to have.

• Work closely with Product to understand customer/business needs, success metrics and priorities to plan out your own solution space and roadmap.
• Develop material recommendation algorithms based on live material inventory, purchase behavior and buying preferences.
• Develop models and algorithms to aggregate, clean, structure and standardize supplier inventory data from various data sources and formats including Excel, PDF, ERP and unstructured natural language.
• Design and analyze large-scale A/B tests to continuously improve and iterate algorithm performance.
• Evaluate algorithm performance to identify improvement opportunities.
• Proactively communicate, share your conclusions, and explain complex topics tailored to diverse audiences.
• Push new ideas into the design and concept funnel while keeping business priorities, trade-offs, and technical feasibility in mind.
• Create, develop, and maintain metrics on dashboards to measure algorithm performance and the value generated for our customers and for the company