Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist with strong Data Engineering skills in Central London for an initial 6-month contract, paying approximately £600 per day. Key requirements include proficiency in Python, SQL, cloud platforms, and experience in financial services.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
640
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🗓️ - Date discovered
September 19, 2025
🕒 - Project duration
More than 6 months
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🏝️ - Location type
Hybrid
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📄 - Contract type
Inside IR35
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🔒 - Security clearance
Unknown
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📍 - Location detailed
London Area, United Kingdom
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🧠 - Skills detailed
#Datasets #Airflow #TensorFlow #Pandas #Visualization #AWS (Amazon Web Services) #Python #Data Exploration #SQL (Structured Query Language) #BI (Business Intelligence) #Scala #Data Warehouse #GCP (Google Cloud Platform) #Plotly #Redshift #Spark (Apache Spark) #Data Lifecycle #Cloud #Data Science #Data Pipeline #BigQuery #Data Wrangling #Libraries #Microsoft Power BI #Storytelling #Deployment #Monitoring #Azure #Snowflake #PyTorch #Data Engineering #dbt (data build tool) #Data Quality #Tableau #NumPy #ML (Machine Learning)
Role description
Data Scientist (Hybrid Data Scientist & Data Engineer)
📍 Central London (Hybrid On-Site) | 💼 Financial Services
I'm looking for a Data Scientist with strong Data Engineering capabilities to join a leading financial services business in Central London on an initial 6 Month Contract basis.
The role is INSIDE of IR35. The Day Rate is yet to be defined but I think we'll be around the £600 P/D mark.
This hybrid role requires on-site presence and is ideal for professionals who can bridge the gap between data engineering and data science, owning the entire data lifecycle from ingestion to production deployment.
Key Responsibilities
• Data Engineering & Collection – Build and maintain scalable data pipelines, ensure data quality, and manage structured/unstructured datasets.
• Data Exploration & Preparation – Perform data wrangling, cleaning, and exploratory analysis to derive business-ready datasets.
• Model Development – Design, develop, and validate statistical models and machine learning solutions to solve complex financial services challenges.
• Productization & Integration – Deploy and integrate models into production systems, ensuring reliability and scalability.
• Monitoring & Maintenance – Track performance of pipelines and models, implementing retraining and performance optimization strategies.
• Communication & Visualization – Present insights clearly through dashboards, reports, and storytelling to both technical and non-technical stakeholders.
Key Skills & Experience
• Proficiency in Python and libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch.
• Strong SQL skills and experience with data warehouses (e.g., Snowflake, BigQuery, Redshift).
• Experience with cloud platforms (AWS, GCP, or Azure) and modern data engineering tools (e.g., Airflow, dbt, Spark).
• Solid understanding of machine learning, statistical modeling, and data science best practices.
• Ability to create impactful data visualizations (e.g., Tableau, Power BI, Plotly).
• Strong problem-solving skills with the ability to communicate insights effectively.
• Experience working in financial services or another highly regulated environment is highly desirable.
Data Scientist (Hybrid Data Scientist & Data Engineer)
📍 Central London (Hybrid On-Site) | 💼 Financial Services
I'm looking for a Data Scientist with strong Data Engineering capabilities to join a leading financial services business in Central London on an initial 6 Month Contract basis.
The role is INSIDE of IR35. The Day Rate is yet to be defined but I think we'll be around the £600 P/D mark.
This hybrid role requires on-site presence and is ideal for professionals who can bridge the gap between data engineering and data science, owning the entire data lifecycle from ingestion to production deployment.
Key Responsibilities
• Data Engineering & Collection – Build and maintain scalable data pipelines, ensure data quality, and manage structured/unstructured datasets.
• Data Exploration & Preparation – Perform data wrangling, cleaning, and exploratory analysis to derive business-ready datasets.
• Model Development – Design, develop, and validate statistical models and machine learning solutions to solve complex financial services challenges.
• Productization & Integration – Deploy and integrate models into production systems, ensuring reliability and scalability.
• Monitoring & Maintenance – Track performance of pipelines and models, implementing retraining and performance optimization strategies.
• Communication & Visualization – Present insights clearly through dashboards, reports, and storytelling to both technical and non-technical stakeholders.
Key Skills & Experience
• Proficiency in Python and libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch.
• Strong SQL skills and experience with data warehouses (e.g., Snowflake, BigQuery, Redshift).
• Experience with cloud platforms (AWS, GCP, or Azure) and modern data engineering tools (e.g., Airflow, dbt, Spark).
• Solid understanding of machine learning, statistical modeling, and data science best practices.
• Ability to create impactful data visualizations (e.g., Tableau, Power BI, Plotly).
• Strong problem-solving skills with the ability to communicate insights effectively.
• Experience working in financial services or another highly regulated environment is highly desirable.
