Senior Data Scientist
Content + Source + Freshness • 14 Dec 2025 • 95% confidence
Offer value
Attractive opportunity in data science, offering cutting-edge work in finance with competitive compensation and growth.
- Engage in significant data-driven projects in finance.
- Work with advanced analytics tools and methodologies.
- Strong career progression prospects in a financial sector leader.
Pros
- High demand for data science roles in the finance sector.
- Opportunities to work with advanced analytics and machine learning.
- Clear pathways for career advancement within a prominent organization.
Cons
- Requires hands-on experience with data science tools and languages.
- Possibility of project ambiguity in fast-paced environments.
- Intensive problem-solving and analytical demands.
Who it's for
Mid-level to Senior • On-site or Hybrid
Good fit
- Experienced data scientists.
- Professionals motivated by data-driven problem solving.
- Individuals keen on innovation in financial services.
Not recommended for
- New entrants to data science without hands-on experience.
- Candidates seeking roles outside finance.
- Those uncomfortable with analytical rigor requirements.
Motivation fit
Key skills
About the job
Control Automation Data Science Sr Analyst (C11) - DS
About CITI
Citi's mission is to serve as a trusted partner to our clients by responsibly providing financial services that enable growth and economic progress. We have 200+ years of experience helping our clients meet the world's toughest challenges and embrace its greatest opportunities.
About AIM:
Analytics and Information Management (AIM) is a global community that is driving data driven transformation across Citi in multiple functions with the objective to create actionable intelligence for our business leaders. We are a fast-growing organization working with Citi businesses and functions across the world.
What do we do:
We have one formula for managing risk across the firm: our Enterprise Risk Management Framework. This consistency enables us to take an end-to-end view in how we identify, measure, build, control and report risks. We are also working to simplify, streamline and automate our manual controls—strengthening our ability to prevent issues, not just to detect them after they happen. When issues do appear, we should strive to understand the root cause. We can then apply those lessons-learned horizontally across the organization to prevent similar issues from arising elsewhere.
Expertise required:
Data Analysis and Modelling:
- Use statistical modelling analysis, Machine Learning, Data mining techniques to extract actionable insights from large and complex datasets.
- Develop model to forecast trends, identify patterns and solve various business challenges.
- Conduct exploratory data analysis to uncover hidden relationships and opportunities for optimizations.
Algorithm Development:
- Design, develop, deploy advance algorithms to solve complex business problems.
- Optimize algorithms for scalability, performance, and accuracy.
- Stay abreast of the latest advancements in Machine Learning and Artificial Intelligence and continuously improve modelling techniques.
Collaboration and communication:
- Collaborate with Model Risk Management and Data Engineers, business analysts, product managers to define project requirements and deliver solutions.
- Communicate findings and recommendations to technical and non-technical stakeholders.
- Act as subject matter expert on Data Science methodology and best practices.
Data management and governance:
- Work with Data engineers to ensure availability, quality, and integrity of datasets.
- Develop and implement Data governance procedures to ensure compliance with regulatory requirements and industry standards.
Domain Skills
- Good understanding of Banking domain (Wealth, Cards, Deposit, Loans & Insurance etc.)
Functional Skills
- Business risk, controls, compliance, and data management.
- Nice to have - Knowledge of Finance Regulations, Understanding of Audit Process
Soft Skills
- Should have good communication and inter-personal skills.
- Mentoring junior members in the team
- Ability to thrive in a dynamic and fast-paced environment.
- Contribute to organizational initiatives in wide ranging areas including competency development, training, organizational building activities etc.
- Proactive approach in solving problems and eye for details.
- A strong team player
Qualifications:
- Bachelor’s degree in quantitative field (Computer science, engineering, mathematics, machine learning, statistics)
- 5-8 years of experience in Data science role
- Proficiency in programming languages like Python, R, or SAS
- Experience in Python and relevant libraries (Numpy, Pandas, Scikit learn etc.)
- Coursework related to Machine learning, Deep learning (NLP / OCR) and Programming
- At least an introductory understanding of LLMs and prompt engineering
- Hands on application experience using common ML frameworks such as TensorFlow, PyTorch, OpenAI and LangChain
- Experience with big data platforms (e.g. Hadoop, Spark) and SQL databases
- Demonstrated ability to write high quality code, develop Machine Learning models
- Excellent verbal and written communication skills
- Problem solving and Story telling skills to provide recommendations and generate actionable Business Insights.
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Job Family Group:
Decision Management------------------------------------------------------
Job Family:
Specialized Analytics (Data Science/Computational Statistics)------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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