? Delivered end-to-end data science projects, including core business ML solutions.
? Researched and implemented state-of-the-art methods for the next generation of AI solution.
This includes deep reinforcement learning, deep learning (LSTM, ordinary feedforward
networks), and gradient boosting. The deep reinforcement learning algorithm
allowed one of our clients to boost collections by about 14%. This resulted in monetary
gains of nearly 65000EUR p.a.
? Developed a contextual bandit algorithm for clients with smaller amounts of data. Average
uplift - 2%.
? Developed a message classi?cation algorithm that allowed to reduce operational workload
for the clients.
? Led a team of 5.
? Created a roadmap for the data science projects.
? Devised data collection/data quality control strategy and collaborated with multiple
teams to re?ne data collection practices.
? Communication of the needs and results of the data science team to internal, as well as
external (Otto Group board members) stakeholders.
? Building solutions to benchmark/backtest the performance of the ML algorithms.
? Building solutions for internal (operations, product managers, etc.) and external (customers)
stakeholders for reporting and performance (KPI) monitoring of the customers'
portfolios.
? Supporting the sales team with ad hoc analyses for customer retention and acquisition.
? Participating in the organization committee for the HamburgAI events.
? Technology stack: Python (and ecosystem, e.g. numpy, tensor ow, keras, sanic as async
microframework) to productionize the model, R (and ecosystem, e.g. ggplot2, data.table,
etc.) for PoC, exploratory analysis + reports. Kubernetes in AWS for deployment to
production.