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Mark NuisMN

Mark Nuis

Senior Data Scientist/Engineer

€800/day
Amsterdam, NL
8-15 years

Average response time: 1 hour

About Mark

During my professional career of over 10 years and an educational background in Econometrics, I have gained experience in various fields with a focus mainly on Data Science, Data Engineering, Quantitative Trading, and Data Management.

Through my experience as an entrepreneur and freelancer, I have developed excellent communication and presentation skills, a strong sense of independence, and the ability to lead, mentor, and collaborate effectively with others. And by working in demanding environments at multiple trading firms, I have developed excellent time management skills and the ability to work under pressure.
  • Dutch

    Native or bilingual

  • English

    Fluent

Can work on-site
Amsterdam (up to 50km), Rotterdam (up to 50km), Utrecht (up to 50km)

Experience

  • ABN Amro Bank- Data Cleansing & Remediation
    Senior Data Engineer (Freelancer)
    BANKING AND INSURANCE
    September 2023 - September 2025 (2 years)
    Amsterdam, Netherlands
    Created ETL pipelines to extract data from numerous sources within the bank and store in structured format within a DataVault solution using Azure Databricks and Azure Data Factory.

    Responsible for enhancing billions of transactions records for all clients used in Credit Risk model redevelopment and IFRS9 reporting every quarter.

    Contributed to the largest data migration within the bank to improve the credit
    proposal process and to capture high quality data on collateral management.

    Created automated data flows to and from consumers to enhance and enrich data attributes used for Credit Risk model redevelopment.

    Implemented automated data quality reports to run daily on incremental data
    before processing in ETL pipelines.
    Azure Databricks Cloud Azure Azure Data Factory Apache Spark ETL
  • Bunker Crypto Trading
    Head of Trading (Co-Founder)
    TECH
    March 2022 - September 2023 (1 year and 6 months)
    Utrecht, Netherlands
    Manages the full Azure stack such as: Kubernetes, Log-analytics, Load-Balancer, Storage accounts, Databricks, Container registry, NAT Gateway, Key Vaults and Managed Identities.

    Built complete automated CI/CD pipeline in Azure Devops with Docker to deploy and continuously update trading strategies on a Kubernetes server in Azure without downtime.

    Developed a fully automated market making software in Rust on Bitvavo, Binance and Bybit for spot and futures and automatically deployed on AWS bare metals.

    Built a FPGA solution to retrieve and parse market data on ultra-low latency
    deployed in AWS.

    Developed a new scalable pair trading strategy that targets an annual unleveraged return of 30% using statistical/econometric models.

    Using PySpark in Azure Databricks to process and store 100 billion records daily.

    Train random forest models on multiple terrabytes of orderbook data to predict certain events.

    Designed a backtesting framework in Databricks that greatly speeds up the research of new strategies and reduces the time to bring them in production.

    Developed a new momentum strategy using a Neural Network to process real-time news messages.
    Azure DevOps PySpark Rust Machine Learning & AI Azure & AWS
  • Webb Traders
    Senior Quantitative Trader
    TECH
    February 2020 - February 2022 (2 years)
    Amsterdam, Netherlands
    Making markets in all European index options and single stock options.

    Operating and monitoring automated trading strategies.

    Responsible for the daily PNL and overall monitoring of risk exposures.

    Utilizing a variety of econometric/statistical models and machine learning models to develop new trading strategies or to enhance existing ones.

    Research, analyse and backtest the new trading ideas on both stock and index
    futures using tick data and in-house cloud computing backtest software (Google Cloud Platform) in Python using Spark distributed computing.

    Implementing non-latency dependent trading strategies in production environment in Python.

    Liaising with developers to convert the latency dependent strategy developed in Python backtest to C++ production code.

    Liaising with risk and compliance to ensure our automated strategies trade within Webb Trader's risk profile and comply with all rules and regulations regarding automated trading.

    Developed and generate daily automated trade reports to improve and analyse the performance of the strategies based on the trades.
    Google Cloud Platform (GCP) Databricks Python Machine learning Econometrics

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Education

  • MSc
    Erasmus University Rotterdam
    2018
    MSc
  • BSc
    Erasmus University Rotterdam
    2016
    BSc

Skill set

Categories