Senior Data Scientist

Barclays UK; Knutsford; On-site; GBP 60,000 - 100,000; Full time

Job summary

An established industry player is seeking a Senior Data Scientist to lead innovative data analytics and machine learning initiatives. This role involves enhancing digital offerings and ensuring superior customer experiences by utilizing cutting-edge technology. As a key member of the Cyber Fraud Fusion Center, you'll develop predictive models, manage data pipelines, and collaborate with stakeholders to drive strategic decisions. If you have a passion for data science and a desire to combat economic crime, this is the perfect opportunity to make a significant impact in a dynamic environment.

Qualifications

• Proven experience as a data scientist in a global organization.
• Strong analytical skills with large datasets and machine learning models.

Responsibilities

• Lead data collection, extraction, and integration from various sources.
• Design and implement statistical and machine learning models.

Skills

Data Mining

Machine Learning

Python

Statistics

Data Analysis

Data Cleaning

Tools

Jupyter Notebook

Hadoop

Spark

REST APIs

Job description

Join us as a Senior Data Scientist at Barclays, where you'll lead the evolution of our digital landscape, driving innovation and excellence. You'll harness cutting-edge technology to enhance our digital offerings, ensuring superior customer experiences. This role involves being an integral part of our Cyber Fraud Fusion Center, delivering scalable services to combat fraud and protect our customers and clients from economic crime.

To succeed as a Senior Data Scientist, you will need:

Essential Skills/Basic Qualifications:

1. Experience working as a data scientist in a global organization, with a broad range of analytical skills from data mining and trend analysis to developing advanced machine learning models. Proven ability to coach data scientists, plan projects, and build relationships with stakeholders.
2. Ability to analyze large structured and unstructured datasets, including internal and external intelligence, fraud, and business data, supporting cybercrime campaign analysis. Practical experience with relational and non-relational databases, Python, Jupyter Notebook, Hadoop, Spark, and REST APIs.
3. Knowledge of descriptive and prescriptive analytics, understanding data distributions, and machine learning algorithms such as regression, clustering, neural networks, and more. Ability to build KPIs based on problem definitions.
4. Statistics knowledge including probability, confidence intervals, hypothesis testing, t-tests, chi-square, and ANOVA.
5. Experience designing and building automated data processes, including batch and real-time systems integrating APIs, databases, SFTP, and queues.

Desirable Skills/Additional Qualifications:

1. Experience with fraud detection/prevention systems, translating fraud tactics into hypotheses and ML features.
2. Knowledge of social engineering tactics used by cybercriminals, especially in scams.
3. Broad knowledge of web, networks, and cybersecurity fundamentals.
4. Experience leading data science teams across multiple locations with multiple projects.
5. Active participation in research and development in data science methodologies and tools.

Assessment may include risk and controls, change management, business acumen, strategic thinking, digital and technological skills, alongside role-specific technical expertise.

This role is based in Knutsford.

Purpose of the role

Utilize innovative data analytics and machine learning to extract insights from the bank's data, informing strategic decisions, improving operations, and fostering innovation.

Accountabilities

1. Data collection, extraction, and integration from various sources.
2. Data cleaning, transformation, and quality assurance.
3. Development and maintenance of data pipelines for automated processing.
4. Design and implement statistical and machine learning models for pattern analysis.
5. Develop predictive models to forecast outcomes and identify risks and opportunities.
6. Collaborate with stakeholders to leverage data for business value.
7. Contribute to strategy, requirements, and policy development, managing resources and continuous improvement.
8. If managing a team, define roles, plan future needs, and lead performance and development efforts.
9. Demonstrate leadership behaviors to foster a thriving environment, including listening, inspiring, aligning, and developing others.
10. Guide technical direction and mentor less experienced team members.
11. Advise senior management on impact and strategic alignment of data science initiatives.
12. Manage risks and ensure controls are in place.
13. Understand organizational functions to support business goals.
14. Collaborate across departments to align with business strategies.
15. Develop solutions through sophisticated analysis and interpretative thinking.
16. Engage in research to inform problem-solving approaches.
17. Build and maintain relationships with internal and external stakeholders, using influencing skills to achieve objectives.

All colleagues are expected to embody Barclays Values: Respect, Integrity, Service, Excellence, and Stewardship, and to demonstrate the Barclays Mindset: Empower, Challenge, and Drive.

About the company

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