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I’m passionate about building intelligent systems that move beyond metrics to deliver real-world impact. My work sits at the intersection of machine learning, data science, and product management — where I identify opportunities, frame hypotheses, test them through customer discovery and interviews, and prioritize what truly matters.
I design data-driven systems that lead to measurable business outcomes.
- Design and evaluate ML solutions that deliver business value
- Translate complex data into actionable decisions
- Collaborate cross-functionally to define & test hypotheses
- Use A/B testing to guide iteration
- Develop models & metrics tied to strategic goals
- Communicate insights clearly through visuals and storytelling
Achievements
Work Experience
⚡ Senior ML Engineer – Sention
2023 – Present
🧠 Anomaly Detection & Battery Health Prediction
Designed and deployed an AI-driven battery health predictor using Autoencoders to detect early anomalies in battery cells. Forecasted degradation patterns pre-failure, enabling proactive maintenance. Owned the full ML lifecycle — data pipelines (Airflow), model training, and deployment (FastAPI + Docker).
→ Resulted in a 15% improvement in fault detection latency, helping secure £1.2M seed funding.
🔍 Clustering & Segmentation for Failure Prediction
Developed unsupervised K-Means and DBSCAN models to segment cells by performance signatures. Identified hidden degradation clusters, producing a new “failure signature” dataset that improved model explainability and engineering insights.
→ Improved interpretability metrics by 20% through feature space visualization and labeling.
🔋 Battery Grading using RNNs & Transformers
Implemented LSTM and Transformer-based models for battery State of Health (SOH) forecasting and grading. Modeled multi-sensor time-series data to predict Remaining Useful Life (RUL), achieving a MAPE of 7.8%.
→ Enabled automated grading for battery packs, improving test efficiency by 28%.
🧩 Sentinel: Full-Stack AI Platform for Battery Intelligence
Designed and launched Sentinel — a full-stack platform integrating model outputs, data, and business metrics.
Frontend: Next.js + Tailwind for investor-ready UI.Backend: FastAPI + Docker + modular ML pipelines.
→ Increased internal AI adoption by 3× and accelerated feature validation cycles.
💼 Business Impact
Positioned Sention as an AI-first energy startup by translating complex ML systems into clear business outcomes. Contributed to investor due diligence, driving technical credibility and funding success.
- Bridged ML research with product vision and strategy.
- Reduced faulty cell utilization by 12%.
- Influenced roadmap for predictive maintenance offerings.
📈 Data Scientist – Digital Futures
2022 – 2023
Analyzed sales and user behavior data to uncover product pain points and prioritize features — directly influencing product roadmaps and improving user satisfaction metrics.
Delivered churn and engagement analyses to leadership, translating insights into actionable roadmap items that increased retention by 22% and optimized feature utilization.
Collaborated cross-functionally with engineering, design, and marketing to implement data-driven experiments that improved product usability and informed go-to-market strategies.
Selected Work
Skills & Education
Machine Learning/ Data Science
- Supervised and Unsupervised ML
- Model Development (TensorFlow, PyTorch, Scikit-Learn)
- MLOps & Experimentation (Weights & Biases, MLflow)
- NLP (Transformers, HuggingFace, LLM Fine-tuning)
- Feature Engineering & Deployment
Data Science
- Exploratory Data Analysis (Pandas, NumPy, Seaborn)
- Statistical Modeling & Forecasting
- A/B Testing, Experiment Design
- SQL, BigQuery, Airflow
- Data Storytelling & Visualization (Plotly, Power BI)
Product Management
- Product Strategy & Roadmapping
- Customer Discovery & UX Research
- Metrics, OKRs, & Feature Prioritization
- Cross-functional Team Leadership
- AI Product Design & Human-centered Systems
Education
Masters in Computer Science
Leeds University