Senior Data Scientist
Machine Learning in production, reliable ML systems, explainability, monitoring, and responsible AI.
Impact Highlights
Profile
I am a Senior Data Scientist with over ten years of experience spanning academic research and industry practice. I specialise in designing, deploying, and monitoring machine learning systems that operate reliably in real-world environments.
My work combines statistical rigour, practical engineering, and clear communication. I focus on systems that are not only accurate, but interpretable, robust, and aligned with organisational goals.
I have worked across insurance, financial services, automotive research, human-centred AI systems, and intelligent games.
Technical Expertise
Machine Learning and AI
- Regression, classification, clustering, ranking models
- Deep learning (CNN, RNN, LSTM, BiLSTM)
- Uncertainty modelling and evidential deep learning
- Experimental design and A/B testing
- Federated learning (research prototyping)
Data and Engineering
- Python (NumPy, Pandas, SciPy, Scikit-learn, TensorFlow, Keras)
- Distributed computing (Dask, Spark)
- SQL (MySQL, PostgreSQL)
- Visualisation (Matplotlib, Plotly, Tableau)
- Version control and collaboration (Git)
Approach to Building Reliable ML Systems
I treat machine learning as a lifecycle, not a modelling exercise. My approach is structured and repeatable:
- Problem framing: clarify decision context, define measurable objectives, align on success criteria.
- Data understanding: assess quality, representativeness, bias, and leakage risks early.
- Model design: balance performance with interpretability and stability from the start.
- Robust evaluation: go beyond accuracy, including stability under shift and calibration where relevant.
- Production and monitoring: implement monitoring for drift, anomalies, and silent degradation.
- Documentation: record assumptions, limitations, and operational guidance for maintainability.
Experience
Senior Data Scientist
- Led end-to-end development of production machine learning systems.
- Delivered interpretable forecasting models to support planning and decision-making.
- Designed monitoring frameworks to detect drift and performance degradation.
- Used distributed computing tools to handle large-scale datasets efficiently.
- Mentored junior data scientists and promoted data literacy initiatives.
Research Assistant
- Applied advanced machine learning and uncertainty modelling in automotive research.
- Investigated evidential deep learning approaches for battery-related applications.
- Contributed to research publications and technical reporting in multidisciplinary teams.
Data Scientist
- Improved predictive model performance by 10% using feature engineering and optimisation.
- Built monitoring tools to detect distributional shifts in deployed systems.
- Delivered analytical reporting to senior stakeholders in global banking.
- Worked across the full lifecycle from research to deployment.
- Prototyped federated learning, explainability, and intelligent labelling approaches.
Lecturer
- Delivered postgraduate teaching in data analysis and machine learning.
- Taught supervised and unsupervised learning, time series modelling, and deep learning.
- Achieved Associate Fellow status with the Higher Education Academy.
Researcher
- Published and presented 10+ peer-reviewed papers.
- Contributed to European H2020 projects (RAGE, AMIGOS).
- Designed intelligent games experiments with 150+ users.
- Developed and evaluated social robotics experiments with 230+ participants.
- Applied statistical analysis methods including ANCOVA and GEE.
Early Career
- Contributed to enterprise software development and research projects.
- Built an intelligent mood detection model reaching 91% accuracy using Random Forest.
- Mentored MSc students during research work.
Speaking & Teaching
I have taught and supported postgraduate students in data analysis and machine learning, with an emphasis on practical implementation, conceptual clarity, and responsible use of models.
- Supervised and unsupervised learning foundations
- Deep learning for time series (CNN, RNN, LSTM)
- Data visualisation and exploratory analysis
- Experimental design and evaluation
- Communicating model behaviour, limitations, and risk to diverse audiences
I have also presented research at international venues and collaborated across multidisciplinary teams.
- AAMAS – International Conference on Autonomous Agents and Multiagent Systems
- HRI – The ACM/IEEE International Conference on Human-Robot Interaction (HRI)
- ACII – International Conference on Affective Computing and Intelligent Interaction
- European H2020 project dissemination workshops (RAGE, AMIGOS)
- Research seminars and technical workshops in AI and intelligent systems
Selected Projects
- SMILE (interpretability): robust model-agnostic explainability, designed to improve stability over standard local explanations.
- Recommender systems: session-based ranking models (LightGBM, CatBoost) with strong benchmark performance.
- Wind forecasting: BiLSTM time series modelling for renewable energy prediction.
- NLP topic extraction: topic modelling and summarisation pipelines for research corpora.
- Healthcare prediction: ensemble models with strong AUROC performance.
Education
- PhD in Computer Science and Engineering (Distinction), Instituto Superior Técnico
- MSc in Artificial Intelligence and Robotics, University of Tehran
- BSc in Computer Engineering, Iran University of Science and Technology
Awards
- ACM Student Travel Grant (AAMAS 2019)
- Student Travel Grant (ACII 2019)
- H2020 Fellowship (RAGE Project)
- Top national ranks in competitive university entrance examinations
Memberships
- IEEE Member
- ACM Professional Member