Bridging data, science & strategy 🚀 Machine Learning 🛠️ Tool Development 📦 R Software 🧭 Leadership 🧬 Life Sciences Domain Expert ✨ Director of Data Science @ Cercle.ai

🔬 Domain expertise: IVF clinical analysis, proteomics, biomarker discovery, predictive modeling

📊 Technical tools: R, machine learning, statistics, experimental design, reproducible research

💪 Strengths: Translating complexity, cross-functional collaboration, storytelling with data

"Making predictions is easy ... making accurate ones is much more difficult." ⎯ Me⎯

I love to solve problems.

Often the problem can be understanding a complex biological process, but it can also be as simple as fixing something that's broken (e.g. a door that jams, a bicycle, or even machine learning software). In particular, I like to apply my data science skills to better understand, or even solve, the problems we face.

I am always open to discuss possible roles 🔭 and whether my skill set can solve problems in your space!

Machine Learning 🚀	Statistics 📊	Open-Source 💻	Software Tools 🔧
Random forest	Regression problems	R	Linux🐧, MacOS 🍎
Naive Bayes	Real-world data (RWD)	C++	Git, GitHub
Lasso regularization	GLMs	CI/CD	AWS
k-Nearest neighbour	Causal inference IPTW	LaTeX	BASH, GNU
PCA	Survival analysis	Python 🐍	Docker 🐋
Maximum-likelihood	Linear mixed-effects	Opencode	LLM/Agentic workflows

• Execute organization's data science strategy, aligning analytics with business and clinical goals
• Instituted a culture of rigorous, reproducible analysis -- shifting the team from reactive one-off requests to disciplined workflows emphasizing data quality as the primary standard
• Deliver causal inference analyses on large-scale reproductive health data (IPTW, propensity scoring, covariate balancing) to inform clinical treatment protocol decisions
• Lead our key pharmaceutical partnerships, translating multi-arm observational results into clinical insights
• Collaborate with customers and C-suite to create framework for data-based decision making
• Architect and maintain a company-wide \R analytics ecosystem -- standardizing workflows from data ingestion through Quarto-driven client reporting
• Build and mentor a data science team of 3-5; own hiring, statistical analysis plans, code review, and technical growth

False Discovery	Mixture Models	Logistic Regression	Naive Bayes
The Birthday Paradox	Mack-Wolfe Tests	Mixed Effects	Monty Hall Paradox
Decision Boundaries	Class Imbalance	Pitch Classifier	IPTW

• 💬 Favorite food: 🐟 🌮
• 📚 I am currently learning woodworking 🪵 ... I'm mostly good at making a lot of sawdust!
• 💬 Ask me about: bikes and R ... I'll talk your 👂 off!
• 🚴 I'm an avid cyclist: come say hi on