About Daniel
I help startups and product teams turn machine learning into real-world impact.
With years of experience across the AI product lifecycle—from data science and MLOps to solution architecture—I’ve supported companies in designing scalable ML systems, optimizing infrastructure, and aligning technical decisions with product goals.
Whether you need strategic guidance, hands-on model deployment, or help scaling your data team, I can jump in at any stage of the journey. I’m used to working closely with CTOs, product managers, and engineering leads to make sure data science initiatives actually deliver.
✅ ML architecture & pipeline design
✅ MLOps & deployment strategy (incl. Snowflake, LightGBM, Optuna, etc.)
✅ Model evaluation, monitoring & scaling
✅ Advisory & team mentoring
Let’s talk about how to get your AI initiatives off the whiteboard and into production.
English
Native or bilingual
Remote only
Primarily works remotely
Experience
- A Retail Insight CompanySenior Decision ScientistNovember 2022 - Today (3 years and 7 months)London, UKDesigned and implemented highly efficient, custom feature extraction pipelines, accelerating time-series model development and deployment.Built robust data pipelines using Pandas and Python’s multiprocessing libraries, initially deploying on Windows VMs before migrating to Snowflake.Re-architected the platform to support billions of rows per day, deploying hundreds of models across multiple clients.Reduced model deployment time for new pilots from months to 2 weeks, achieving a 100% conversion rate from trial to full client adoption.Expanded the product to serve new verticals (e.g. retail specialists vs. grocery), growing TAM and strategic value to investors.Delivered 85% precision at scale, generating millions in incremental revenue by sending thousands of ML-driven signals daily.Built software with test-driven development and modular, reusable functions, streamlining the research-to-production cycle.Scaled the solution to support 2,500+ stores, 40,000+ SKUs, and over 100 million daily 80-day time series predictions.Conducted deep experimentation with LightGBM, XGBoost, CatBoost, and NGBoost, optimizing based on problem uncertainty.Developed a novel multi-level modeling architecture to handle high-cardinality categorical variables.Tackled complex ML challenges including asymmetrically noisy labels and semi-supervised learning for time-series classification.Line-managed and mentored junior decision scientists, growing team capabilities.
- MicrogeneticsProduct ManagerNovember 2020 - November 2022 (2 years)Corsham, UKProduct-managed an enterprise data platform for monitoring pharmaceutical cleanrooms, ensuring compliance and real-time insights for critical lab environments.Led a cross-functional team of 20+, including engineers, QA, data scientists, customer success, design, marketing, and sales.Played a key role in pivoting the company strategy, successfully targeting new labs and securing the first sales to external pharma customers.Owned the product backlog, defining and prioritizing features with clear, actionable acceptance criteria for engineering.Championed a lean, Agile development process, focusing on rapid iteration, customer feedback, and continuous course correction to accelerate product-market fit.
- MicrogeneticsData ScientistApril 2019 - November 2020 (1 year and 7 months)Corsham, UKBuilt and deployed a colony counting computer vision system for pharmaceutical cleanrooms, solving a key automation need in a highly regulated environment.Researched and prototyped solutions using YOLO and ResNet for object detection and segmentation, and applied Siamese Neural Networks for few-shot classification where training data was limited.Deployed the final solution as a Flask-based microservice on AWS, enabling scalable, API-driven integration with lab systems.
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Education
- PhDUniversity of Bath2012PhD in Computer Science