Data Scientist

Bdo; Greater London; On-site; GBP 111,000 - 185,000; Full time

Job summary

BDO Regulatory Solutions in London is recruiting a Data Scientist on an initial contract from November to 31 March 2027. You will work with the delivery team to develop models, document them, and productionise services, applying AI/ML, Python, SQL and time-series analysis.

This role requires strong ML deployment knowledge (Podman, SageMaker, DSW pipelines) and version control with Git, plus data wrangling and EDA to identify risk signals.

Qualifications

• Strong knowledge and experience of AI and ML; Python (pandas, NumPy, scikit-learn) for data wrangling, modelling and feature engineering.

Responsibilities

• Work with the delivery team to develop models, document them and productionise services.
• Collaborate on model development and validation; apply time-series analysis to assess risk trends.
• Support deployment and monitoring of ML models; maintain reproducible workflows with Git.

Skills

Python

Pandas

NumPy

scikit-learn

SQL

Time-Series

EDA

Git

Containerised deployment

Tools

Podman

SageMaker

DSW pipelines

Job description

BDO Regulatory Solutions are currently recruiting for a Data Scientist to join our client, a Regulated firm, based in London. We are offering an initial contract starting in November through to 31st March 2027 initially with an excellent day rate, employed via an Umbrella company. We are looking for talented and experienced data scientists with experience to join our programme. To work with the existing delivery team to deliver models, documentation and associated productionised services. Solid knowledge and experience of AI and ML is essential, Python (pandas, NumPy, scikit-learn): For data wrangling, modelling, and feature engineering. SQL: For querying structured data sources. Model Development & Validation: Experience with classification, unsupervised learning (e.g. outlier detection), and ranking models. Machine Learning Deployment: Familiarity with containerised deployment (e.g. Podman, SageMaker, DSW pipelines). Version Control (Git): To maintain reproducible and collaborative workflows. Time-Series Analysis: To assess risk trends over financial years. Exploratory Data Analysis (EDA): To spot early signals or risk clusters.

• Solid knowledge and experience of AI and ML is essential, Python (pandas, NumPy, scikit-learn): For data wrangling, modelling, and feature engineering.
• SQL: For querying structured data sources
• Model Development & Validation: Experience with classification, unsupervised learning (e.g. outlier detection), and ranking models.
• Machine Learning Deployment: Familiarity with containerised deployment (e.g. Podman, SageMaker, DSW pipelines)
• Version Control (Git): To maintain reproducible and collaborative workflows.
• Time-Series Analysis: To assess risk trends over financial years.
• Exploratory Data Analysis (EDA): To spot early signals or risk clusters.

Desirable Experience:

• Rank Aggregation/Ensemble Techniques: Understanding methods like Robust Rank Fusion (RRF)
• Model Explainability Tools: e.g. SHAP, LIME to support interpretability.
• Experience with Model Monitoring & Drift Detection
• Experience in RegTech / FinCrime / Data-led Supervision Projects is a plus.
• Experience developing solutions for record linkage and/or network analytics tasks.
• Experience with graph query languages (e.g., Gremlin, Cypher), graph database platforms (e.g., Neptune, Neo4j), and/or graph visualisation platforms