Senior Data Scientist

Senior Data Scientist - GenAI & ML

CRU Group is seeking a technically strong, hands-on Senior Data Scientist to joi...

Location

United Kingdom , London

Salary:

Not provided

CRU International

Expiration Date

Until further notice

Requirements

MSc or higher in a quantitative discipline such as econometrics, statistics, mathematics, physics, or similar
Expert-level Python skills and strong command of the core scientific stack, including Pandas, Scikit-learn, SciPy, and NumPy
3–5+ years of experience in data science, machine learning, econometrics, or a related technical field
Proven track record of independently delivering solutions, including model design, training, evaluation, deployment, and monitoring
Strong exposure to time-series forecasting or commodity data
Creative thinker with a robust approach to data and modelling
Hands-on experience working with machine learning and LLMs
Hands-on experience building AI applications beyond simple chatbot interfaces
Goal-oriented, with a high level of energy and enthusiasm
able to operate efficiently, effectively, and professionally in a fast-paced, dynamic, results-driven business environment

Job Responsibility

Build and deliver solutions across predictive analytics, machine learning, and LLM-based AI
Design, develop, and implement bespoke ML and AI agents and multi-step workflows using modern frameworks such as LangChain, LangGraph, or similar technologies
Develop robust, scalable, production-grade Python code
Partner with senior stakeholders to translate business needs into clear technical requirements and measurable outcomes
Own delivery end-to-end, including problem framing, data discovery, model development, evaluation, integration, deployment, monitoring, and iteration
Collaborate with global teams of analysts, product managers, product modellers, and engineers to deliver high-quality, reliable solutions
Mentor team members and contribute to raising standards for technical execution and delivery discipline
Stay current with developments in machine learning, statistical learning, generative AI, LLMs, and agentic workflows, and identify practical opportunities to apply these technologies within risk and trading services

What we offer

Competitive salary and flexible benefits package
Opportunities for professional growth and development as part of a global company
A collaborative and supportive work environment
The chance to work with industry-leading experts and over a diverse range of topics and projects

Fulltime

Senior Data Scientist – GenAI & Traditional AI

We are seeking a Senior Data Scientist with strong hands-on experience across Tr...

Location

India , Hyderabad

Salary:

Not provided

NStarX

Expiration Date

Until further notice

Requirements

5+ years of hands-on experience in data science / machine learning with delivered outcomes
Practical experience in both Traditional ML (supervised/unsupervised methods, NLP, time series, recommendations, etc.) and GenAI (LLM-based features)
Hands-on experience building or contributing to RAG systems
familiarity with agentic frameworks is strongly preferred
Solid understanding of model evaluation, experimentation rigor, and iterative improvement cycles
Ability to independently own tasks within a project and deliver with minimal supervision
Experience mentoring or guiding junior team members through reviews and coaching
Strong communication and documentation skills

Job Responsibility

Execute end-to-end AI/ML experimentation for a single project: problem framing, dataset readiness, feature engineering, model training/tuning, evaluation, and iteration
Build and optimize GenAI pipelines using proprietary and open-source models depending on project needs
Contribute to LLM-driven feature development with working knowledge of model behavior, embeddings, prompting strategies, context management, and evaluation
Implement and improve RAG pipelines (document ingestion, chunking strategies, embeddings, retrieval, reranking, grounding, and evaluation)
Develop agentic workflows using modern agent frameworks and tool integrations (multi-step execution, tool use, safety/guardrails)
Assist in defining evaluation methods for GenAI (faithfulness, relevance, hallucination risk, latency, cost)
Work with engineering and MLOps teams to transition experiments into production-grade pipelines
Contribute to performance tuning, observability, and reliability of AI services (metrics, logging, monitoring, error analysis)
Ensure implementation follows best practices for maintainability, scalability, and cost efficiency
Guide and groom junior data scientists through structured mentorship: task planning, implementation reviews, and feedback

Fulltime

Senior GenAI Data VP – Market Data

Embark on a transformative journey as a Senior GenAI Data VP – Market Data at Ba...

Location

United States , Whippany

Salary:

170000.00 - 230000.00 USD / Year

Barclays

Expiration Date

Until further notice

Requirements

Experience with design and delivery of GenAI data architectures, including agentic workflows, RAG pipelines, prompt orchestration, and tool calling frameworks
Ability to manage strategy and integration of market data platforms (FactSet, LSEG, Bloomberg, S&P Capital IQ), ensuring high quality ingestion, normalization, enrichment, and access for analytics and GenAI use cases
Experience with designing and overseeing scalable GenAI and machine learning data pipelines, supporting structured and unstructured data across the full AI lifecycle
Ability to establish and embed data quality, lineage, privacy, and compliance controls to ensure responsible AI use and regulatory alignment
Stakeholder management skills to support business, product, engineering, and data science teams to identify high impact GenAI use cases and translate them into production grade solutions, defining best practices for evaluation metrics, MCP integration, and scalable data integration across GenAI systems

Job Responsibility

Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data
Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures
Development of processing and analysis algorithms fit for the intended data complexity and volumes
Collaboration with data scientist to build and deploy machine learning models

What we offer

medical, dental and vision coverage
401(k)
life insurance
other paid leave for qualifying circumstances
incentive award
competitive holiday allowance
private medical care
pension contribution

Fulltime

We are looking for an experienced Senior Data Scientist to drive advanced analyt...

Location

Kosovo

Salary:

Not provided

Valtech

Expiration Date

Until further notice

Requirements

Strong experience in machine learning, statistics, and applied data science
Experience with causal inference, experimentation, or decision science methodologies
Solid understanding of forecasting, optimization, or analytical modeling techniques
Strong programming skills in Python and SQL
Experience building and deploying production-ready data science or ML systems
Familiarity with model lifecycle management (training, deployment, monitoring)
Hands-on experience with at least one major cloud platform: Azure (preferred), AWS, or GCP
Experience working with modern data and AI platforms (e.g., Azure ML / Azure AI, Databricks, or similar ecosystems)
Experience working with complex, multi-source datasets (e.g., transactional, behavioral, operational data)
Ability to translate business problems into analytical frameworks

Job Responsibility

Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems)
Apply statistical, predictive, and prescriptive modeling techniques to solve business problems
Build reusable modeling frameworks that can scale across multiple domains
Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods)
Translate observational and experimental data into actionable business insights
Embed causal reasoning into decision systems that guide actions (e.g., optimization, prioritization, trade-offs)
Integrate GenAI capabilities (e.g., LLMs, RAG pipelines, agent-based systems) into data science workflows
Contribute to the development of intelligent agents and AI-assisted decision-making systems
Combine structured data models with unstructured data and GenAI outputs
Build forecasting models (time-series, probabilistic, causal) to support planning and operations

What we offer

Health insurance that guarantees fast access to contracted health services
Vacation Plan
Subsidy for study materials, trainings, conferences and events that will contribute to your development
Hybrid Working model
Performance Evaluation Process that paves the roadmap for a personal and professional career development
Refreshments and fruit in the office
Team gatherings and parties organized and subsidized by the company

Fulltime

Senior Data Scientist, AI & Model Risk

It all started with an idea at Block in 2013. Initially built to take the pain o...

Location

United States , Bay Area

Salary:

139000.00 - 225000.00 USD / Year

Cash App

Expiration Date

Until further notice

Requirements

A minimum of 5 years of related experience with a Bachelor’s degree in a quantitative field
or 3 years and a Master’s degree
or a PhD without experience
or equivalent work experience in risk management, model risk management, or AI risk management
Proficiency in Python or similar languages for evaluating AI system behavior, writing test scripts, or analyzing model outputs
Strong understanding of generative AI architectures
Including LLMs, transformer models, RAG systems, and agentic AI, plus hands-on experience interacting with and critically evaluating these systems, sufficient to assess design decisions, output quality, and limitations
Understanding of interagency model risk management principles, including SR 26-2
Knowledge of AI testing methodologies, ex. functional testing, bias testing, adversarial testing, and performance monitoring plus familiarity with data privacy and security principles (encryption, access controls, data classification)
Excellent written and verbal communication and the ability to translate complex technical AI concepts for non-technical stakeholders, senior management, and regulators

Job Responsibility

Lead end-to-end AI Risk Assessments for generative AI and LLM use cases across the Bank
Embedding in Block's enterprise-wide GenAI review process, coordinating cross-functional SMEs (Legal, Compliance, InfoSec, Data Governance, MRM, ERM, BRC, TPRM, Financial Crimes), and managing timelines to ensure reviews are completed within SLA
Review AI system design and documentation
Including retrieval sources, assumptions, limitations, fallback plans, guardrail configurations, and change management procedures — across banking use cases such as fraud detection, BSA/AML compliance, credit decisioning, and customer-facing applications, ensuring governance controls are commensurate with each use case's risk profile
Assess pre-deployment testing for adequacy inclusive of output integrity, hallucination detection, boundary and edge case testing, ethical and safety guardrails, bias testing, A/B testing, volume testing, and UAT — designing and conducting independent testing as needed
Evaluate ongoing monitoring plans for comprehensiveness - including accuracy, hallucination rates, drift detection, sensitive data controls, reliability metrics, CSAT, acceptable performance ranges, and documented remediation procedures
Develop and maintain templates, tools, and procedures to support the effectiveness and scalability of the AI Risk Governance Program
Monitor the evolving regulatory landscape for AI in banking — including FFIEC IT Examination Handbook standards, FDIC Financial Institution Letters, interagency statements, and the anticipated RFI on AI model risk management referenced in SR 26-2 — and incorporate emerging guidance into the AI risk governance program
support SFS's response to regulatory inquiries as needed

What we offer

Remote work
medical insurance
flexible time off
retirement savings plans
modern family planning

Fulltime

Senior Data Scientist

We are looking for an experienced Senior Data Scientist to drive advanced analyt...

Location

Poland

Salary:

Not provided

Valtech

Expiration Date

Until further notice

Requirements

Strong experience in machine learning, statistics, and applied data science
Experience with causal inference, experimentation, or decision science methodologies
Solid understanding of forecasting, optimization, or analytical modeling techniques
Strong programming skills in Python and SQL
Experience building and deploying production-ready data science or ML systems
Familiarity with model lifecycle management (training, deployment, monitoring)
Hands-on experience with at least one major cloud platform: Azure (preferred), AWS, or GCP
Experience working with modern data and AI platforms (e.g., Azure ML / Azure AI, Databricks, or similar ecosystems)
Experience working with complex, multi-source datasets (e.g., transactional, behavioral, operational data)
Ability to translate business problems into analytical frameworks

Job Responsibility

Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems)
Apply statistical, predictive, and prescriptive modeling techniques to solve business problems
Build reusable modeling frameworks that can scale across multiple domains
Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods)
Translate observational and experimental data into actionable business insights
Embed causal reasoning into decision systems that guide actions (e.g., optimization, prioritization, trade-offs)
Integrate GenAI capabilities (e.g., LLMs, RAG pipelines, agent-based systems) into data science workflows
Contribute to the development of intelligent agents and AI-assisted decision-making systems
Combine structured data models with unstructured data and GenAI outputs
Build forecasting models (time-series, probabilistic, causal) to support planning and operations

What we offer

24 working days of paid vacation
National holidays covered
Sick leave (up to 20/year)
Unpaid leave (up to 20/year)
Medical insurance
Multisport card OR Multikafeteria
Maternity & paternity leave support
Internal workshops & learning initiatives
Professional certifications reimbursement
Participation in professional local & global communities

Fulltime

Senior Data Scientist

Location

Ukraine

Salary:

Not provided

Valtech

Expiration Date

Until further notice

Requirements

Strong experience in machine learning, statistics, and applied data science
Experience with causal inference, experimentation, or decision science methodologies
Solid understanding of forecasting, optimization, or analytical modeling techniques
Strong programming skills in Python and SQL
Experience building and deploying production-ready data science or ML systems
Familiarity with model lifecycle management (training, deployment, monitoring)
Hands-on experience with at least one major cloud platform: Azure (preferred), AWS, or GCP
Experience working with modern data and AI platforms (e.g., Azure ML / Azure AI, Databricks, or similar ecosystems)
Experience working with complex, multi-source datasets (e.g., transactional, behavioral, operational data)
Ability to translate business problems into analytical frameworks

Job Responsibility

Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems)
Apply statistical, predictive, and prescriptive modeling techniques to solve business problems
Build reusable modeling frameworks that can scale across multiple domains
Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods)
Translate observational and experimental data into actionable business insights
Embed causal reasoning into decision systems that guide actions (e.g., optimization, prioritization, trade-offs)
Integrate GenAI capabilities (e.g., LLMs, RAG pipelines, agent-based systems) into data science workflows
Contribute to the development of intelligent agents and AI-assisted decision-making systems
Combine structured data models with unstructured data and GenAI outputs
Build forecasting models (time-series, probabilistic, causal) to support planning and operations

What we offer

Medical insurance
Sports reimbursement budget
Home office support
A number of free psychological and legal consultations
Maternity and paternity leave support
Internal workshops and learning initiatives
English language classes compensation
Professional certifications reimbursement
Participation in professional local and global communities
Growth Framework to manage expectations and define the steps to move towards the selected career

Fulltime

Senior Data Scientist

We are looking for an experienced Senior Data Scientist to drive advanced analyt...

Location

Portugal , Lisbon

Salary:

Not provided

Valtech

Expiration Date

Until further notice

Requirements

Strong experience in machine learning, statistics, and applied data science
Experience with causal inference, experimentation, or decision science methodologies
Solid understanding of forecasting, optimization, or analytical modeling techniques
Strong programming skills in Python and SQL
Experience building and deploying production-ready data science or ML systems
Familiarity with model lifecycle management (training, deployment, monitoring)
Hands-on experience with at least one major cloud platform: Azure (preferred), AWS, or GCP
Experience working with modern data and AI platforms (e.g., Azure ML / Azure AI, Databricks, or similar ecosystems)
Experience working with complex, multi-source datasets (e.g., transactional, behavioral, operational data)
Ability to translate business problems into analytical frameworks

Job Responsibility

Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems)
Apply statistical, predictive, and prescriptive modeling techniques to solve business problems
Build reusable modeling frameworks that can scale across multiple domains
Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods)
Translate observational and experimental data into actionable business insights
Embed causal reasoning into decision systems that guide actions (e.g., optimization, prioritization, trade-offs)
Integrate GenAI capabilities (e.g., LLMs, RAG pipelines, agent-based systems) into data science workflows
Contribute to the development of intelligent agents and AI-assisted decision-making systems
Combine structured data models with unstructured data and GenAI outputs
Build forecasting models (time-series, probabilistic, causal) to support planning and operations

What we offer

Flexibility, with remote and hybrid work options (country-dependent)
Career advancement, with international mobility and professional development programs
Learning and development, with access to cutting-edge tools, training and industry experts

Fulltime

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