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Professional Experience


  • I help teams and companies deliver value-driven solutions to data science problems, fast!
  • I have hands-on experience in solving real business-problems using techniques from descriptive analytics, Bayesian inference, time series forecasting, recommender systems, reinforcement learning, and many others.


👨🏽‍🏭 Machine Learning Engineer

🌍 Amsterdam, Netherlands

🗓️ Jan 2019 - Present

As a Machine Learning Engineer, I am part of Tiqets‘ core Data Team. I work closely with data and business analysts, data engineers, as well as product owners and management team. I apply software development, data analytics, and machine learning to scale and operationalise statistical models and make the whole organisation more data-driven.

📈 Time Series Forecasting

Operationalised sales forecasting by building a general and automated pipeline for pre-processing, model selection, evaluation, and periodic updating of forecasted values. Any of these jobs are distributed across Celery workers running on pods in our Kubernetes cluster. Forecasted values are then exposed to our DWH and can be visualised in our BI tool. We used DataDog for monitoring. FYI: for the most part, LSTM neural networks did not yield better results than classic approaches such as ARIMA and Exponential Smoothing, while an optimised Prophet model performed best.

Technologies: Kubernetes, ECR, S3, Redshift, SQLAlchemy, Alembic, DataDog, Celery, Redis, Dynaconf, FBProphet, mypy, pytests, scikit-learn, sphinx, statsmodels, pandas, numpy

🎥 Recommender System

Improved recommendations across our platform by taking user-preference and item-similarity into account. Also enriched popularity-based heuristics for cold-start instances by taking distance and recency into account. A curated version of an Amazon Personalize model now powers great part of recommendations at Tiqets. To help the Data Team iterate faster and with greater confidence on new versions of the recommender models, we also implemented a time-dependent offline evaluation pipeline for recommender systems, curated for the e-commerce setting.

🏆 Learning to Rank with Reinforcement Learning

To optimise any ranked list on our platform, I initially framed this task as a Supervised Machine-Learned Ranking problem, which involved comparing the predicted ranking to some ideal ranking using a metric such as nDCG. To improve on this iteration, I implemented Bayesian Reinforcement Learning to Rank bandit strategies ( e.g. Thompson sampling ), which use explore and exploit to continuously learn and improve the rankings. As new data is collected through our event-pipeline, we use Airflow to frequently update the rankings (e.g. by re-sampling from the updated posterior distribution).

👨🏽‍💼 Leadership / Management / Soft Skills

  • Provided guidance and supervision to two University students working on their Masters Thesis. Both students finished with outstanding grades (8/10 and 8.5/10).
  • Integrated ideas from methodologies such as CRISP-DM to bring structure to the Data Science project lifecycle. Helped creating and prioritising the Data Science backlog, as well as making weekly meetings more fruitful and actionable.
  • Helped management (CEO, COO, and CTO) in brainstorming sessions, ad-hoc analysis, and reporting.
  • Lead several company-wise trainings in general analytical competency and advanced BI (Looker) practice.
  • The Data Crew at Tiqets grew from 3 to 15 people since I joined. I helped with the recruiting, assessing, and interviewing of candidates as well as organising and attending career fairs.


👨🏽‍🏭 Machine Learning Engineer

🌍 Gildford, UK

🗓️ Jun 2016 - Mar 2018

As a Machine Learning Engineer, I worked directly with the CEO and Lead Developer at Accelogress Ltd , a software consultancy company developing solutions using machine learning and API technologies. For the Save-a-Space project, I lead the development of (1) the forecasting engine for car park availability - using a robust machine learning framework - and (2) the pre-processing engine and REST API to expose historical, real-time, and forecasted availability for multiple car-parks around the UK, to our mobile app and web dashboard.

Technologies: Docker, nginx, Gunicorn, Django REST Framework, AngularJS, scikit-learn, MySQL