Future Anthem

Senior Machine Learning Engineer

Credit Suisse

• Led the development of a new risk escalation & reporting tool for Credit Suisse (UK and US branches). Planned long-term vision, coordinated data acquisition, and built the technological infrastructure
• Led the development of a proprietary graph-analysis application to identify and track the critical dependencies between the data generated by several Credit Suisse departments (EMEA, UK and Swiss branches)
• Designed and implemented a PySpark algorithms working on 10GB of structured batch data ingested daily to identify and escalate potential event driven risks for the Risk management & Compliance department
• Designed and implemented a real-time anomaly detection tool (in Python and Scala) for the Production support team to identify and escalate potential data quality issues

Technologies used: Java (Spring, Hibernate, GraphQL), JavaScript (React, Redux), Python (Pandas, SciPy, Statsmodels), Spark, Hive, Neo4J, Oracle DB, Docker, Kubernetes, Jenkins, Amazon Web Services

Senior Data Engineer

Clear AI

• Performed the PPE demand forecasting using statistical methods in Python (MA, ARMA, VAR). The results were used by the NHS Trusts to optimise their supply and decrease costs during the 1st Covid-19 wave in the UK
• Utilized Python to implement a recommender system (based on both collaborative filtering and content-based approaches) for the Nestlé Canada online store, which boosted the total B2B sales by 25%

Technologies used: Python (Pandas, NumPy, SciPy, Statsmodels, PyTorch, NLTK, Matplotlib, Flask), JavaScript (React, Redux, jQuery), Spark, Hive, Neo4J, PostgreSQL, Docker, Google Cloud Platform

HUBX

• Developed an HTML crawler to download, tag and arrange private companies’ information released by the UK Gov. Trained models for text segmentation and keyword extraction
• Utilized Python to implement several machine learning techniques (SVM, Random Forest, XGBoost) for private companies’ valuation on 30GB of unstructured data, which reduced the total process time by 18%

Technologies used: Python (Pandas, NumPy, SciPy, Statsmodels, PyTorch, NLTK, Matplotlib, Django), Spark, Neo4J, MongoDB, MySQL, Docker, Amazon Web Services

Machine Learning Engineer

Exprivia Spa

• Developed several Spark jobs on high performance computing Linux cluster to automate the systemic risk modelling for the Risk management & Compliance department of UniCredit Spa
• Analysed client export datasets by identifying key correlating metrics and conducting time series analysis using R

Technologies used: Python (PySpark, SciPy), Java (Hibernate), Scala, R, Spark, Hive, Kafka, Solr, Cloudera

Data Engineer

Highly accomplished Problem Solver with a proven track record in the dynamic world of the Tech industry. Specialising in Data Engineering, Data Science, and MLOps, I deliver impactful solutions that drive business success. Motivated by results and fueled by a drive for excellence, I thrive on tackling new challenges and pushing the boundaries of what's possible.

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