Data Science Lead
Fitch Group, Inc.
full-time
Posted on:
Location Type: Hybrid
Location: London • United Kingdom
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Job Level
About the role
- Own the technical roadmap, setting standards for data quality, feature engineering, model governance to ensure scalable, reliable delivery.
- Mentor and upskill the data science team, guiding best practices in experimentation, causal inference, NLP/LLM, and time-series forecasting, review code and models for rigor and reproducibility
- Establish responsible AI practices, including bias testing, explainability, performance monitoring, and documentation; collaborate with legal/compliance on data usage and model transparency.
- Prototype and test new approaches for extracting insights from structured and unstructured data for our core customer base
- Develop and maintain robust ML and data pipelines for experimentation and deployment.
- Design, build, and optimize risk models for analytics and generative AI applications using our proprietary NLP data generation process.
- Collaborate cross functionally with Economists, Industry Analysts, Political Scientists, and Developers.
- Explain model outputs and methodologies to non-technical stakeholders.
Requirements
- Experience setting standards, code review, elevating best practices, hiring and developing talent, and fostering a culture of rigor, collaboration, and delivery.
- Proven experience translating business problems into measurable AI solutions, defining success metrics, prioritizing roadmaps, and driving adoption and impact.
- Technical communication skills explaining complex models, uncertainty, and trade-offs to non-technical audiences; creating clear documentation.
- Substantial experience querying, cleaning, compiling, and analyzing big data.
- Familiarity applying various computational social science methods including data mining, data visualization, natural language processing, text analysis, and basic time series forecasting and machine learning models.
- Familiarity with scenario analysis/stress-testing, simulation analysis, rare event modeling, and stochastic modeling preferred but not required.
- Substantial experience with Python, R, and relevant libraries (e.g., numpy, pandas, scikit, pytorch, tidyverse, caret, ggplot, etc.).
- Proven experience developing, refining, and monitoring NLP models.
- Familiarity with database management tools and techniques (e.g., SQL, Selenium, S3, Sagemaker, API protocols) is preferred but not required.
- Understanding model evaluation methods and metrics.
- Ability to operationalize non-technical ideas into relevant research designs, features, and model outputs.
- Familiarity with experiment tracking and model management tools (e.g., DVC, Weights & Biases).
- Demonstrated experience with interpretable AI techniques.
Benefits
- Hybrid Work Environment: 3 days a week in office required
- A Culture of Learning & Mobility: Dedicated trainings, leadership development and mentorship programs designed to ensure that your time at Fitch will be a continuous learning opportunity
- Investing in Your Future: Retirement planning and tuition reimbursement programs that empower you to achieve your short and long-term goals
- Promoting Health & Wellbeing: Comprehensive healthcare offerings that enable physical, mental, financial, social, and occupational wellbeing
- Supportive Parenting Policies: Family-friendly policies, including a generous global parental leave plan, designed to help you balance career and family life effectively
- Inclusive Work Environment: A collaborative workplace where all voices are valued, with Employee Resource Groups that unite and empower our colleagues around the globe
- Dedication to Giving Back: Paid volunteer days, matched funding for donations and ample opportunities to volunteer in your community
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
data qualityfeature engineeringmodel governanceNLPtime-series forecastingbig data analysisdata miningdata visualizationmachine learninginterpretable AI techniques
Soft Skills
mentoringtechnical communicationcollaborationcode reviewbest practicesfostering cultureexplaining complex modelsguiding experimentationtranslating business problemsoperationalizing ideas