Data Science - Structures Job at Airbus (Bangalore)

Physician Director Oncology Data Science - Translational Science

Are you looking for an opportunity to lead a Translational Data Science team wit...

Location

United States , Collegeville, Pennsylvania

Salary:

Not provided

GSK

Expiration Date

Until further notice

Requirements

medical degree
Completion of a clinical residency program leading to board qualification or certification in internal medicine, pathology, pediatrics is required
10 years plus academic or industry experience in Computational Biology, Bioinformatics, or Translational/Clinical AI with focus on Oncology
5 years of leadership or matrix leadership experience in Computational Biology, Applied AI/ML, Cancer Biology or a related field

Job Responsibility

Lead and build a growing team of Translational Data Scientists to support and innovate the Oncology portfolio pipeline
Leverage your clinical training and experience to translate complex computational and molecular insights into clinically meaningful hypotheses, study designs, and biomarker strategies that directly inform patient care and oncology clinical development
Contribute scientific expertise and design to multiple drug discovery or development efforts across oncology
Act as a competent and recognized expert in developing and critiquing the scientific validity of research/development initiatives to drive the development and delivery of long-term scientific strategy in Oncology Genomics
Individually contribute with analyses and integration of complex cancer datasets. This includes analysis of histopathology imaging data, gene & protein expression (bulk + single cell + spatial), somatic mutations, copy number alterations, and structural variants
Apply statistical methods and AI/machine learning algorithms to identify patterns in data, predict drug response, and discover potential biomarkers. Critically evaluate results. Conduct survival analysis to assess the impact of various factors on patient outcomes
Lead the development and use of bioinformatics pipelines
Serve as a recognized leader in driving technological foresight within specific scientific function or directing content of programs
Lead and influence the outcome of multidisciplinary meetings including partners in therapeutic research units, translational and clinical development teams.
Communicate complex scientific findings clearly to both technical and non-technical audiences.

What we offer

Competitive base salary
Annual bonus based on company performance
Flexible working options available for most roles
Learning and career development
Access to healthcare & wellbeing programmes
Employee recognition programmes

Fulltime

New

Senior Data Science Consultant - Enterprise Complaints, Remediations & Loudspeaker

Wells Fargo Enterprise Complaints, Remediations and Loudspeaker Analytics (ERA) ...

Location

United States , Chandler; Charlotte; Irving; San Antonio; Shoreview; West Des Moines

Salary:

119000.00 - 206000.00 USD / Year

Wells Fargo

Expiration Date

June 19, 2026

Requirements

4+ years of data science experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Master's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science

Job Responsibility

Lead hands-on Generative AI experimentation, including prompt engineering, prompt library development, and agent-style workflows that support voice-of-customer understanding, issue identification, and decision support
Design and execute systematic testing of LLM outputs across large collections of historical customer interaction data, evaluating behavior across tasks, data conditions, and edge cases
Conduct deep error analysis of GenAI outputs, identifying hallucinations, weak or missing evidence, false positives, false negatives, and ambiguity, and translate findings into targeted prompt and system improvements
Develop and apply GenAI evaluation frameworks, including rule-based heuristics, statistical indicators, and LLM-as-a-Judge techniques, to assess output quality, consistency, and risk
Build and refine confidence and uncertainty scoring mechanisms for LLM decisions to support prioritization and secondary human review in higher-risk scenarios
Apply machine learning and NLP models where appropriate to complement GenAI solutions, such as feature extraction, classification, clustering, or signal generation
Analyze complex structured and unstructured datasets to generate hypotheses, surface emerging risks, and identify opportunities where GenAI can augment or automate decision workflows
Collaborate closely with product teams, engineers, and business stakeholders to align GenAI experimentation with operational workflows, risk tolerance, and real-world constraints
Produce clear documentation of prompts, experiments, evaluation methods, and findings to ensure transparency, repeatability, and knowledge sharing
Communicate GenAI behaviors, trade-offs, limitations, and risks effectively to non-technical stakeholders, helping set appropriate expectations for usage

What we offer

Health benefits
401(k) Plan
Paid time off
Disability benefits
Life insurance, critical illness insurance, and accident insurance
Parental leave
Critical caregiving leave
Discounts and savings
Commuter benefits
Tuition reimbursement

Fulltime

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Avp Fraud Analytics - Data Science & Gen Ai

The Specialized Analytics Sr Anlst is a seasoned professional role. Applies in-d...

Location

India , Pune

Salary:

Not provided

Citi

Expiration Date

Until further notice

Requirements

Bachelor's or Master's degree in computer science, Data Science, Machine Learning, or a related field
8+ years of experience in machine learning and deep learning, with a focus on generative AI solution design and development
Strong proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or Keras
Strong background of Prompt Engineering, building agentic AI / AI Agents, API / MCPs for large scale automations in banking domain
Experience with natural language processing (NLP) and natural language generation (NLG)
Proven track record of building and deploying generative models based solutions in production environments particularly using RAG frameworks
Solid understanding of machine learning algorithms, data structures, and software engineering principles
Excellent problem-solving skills and the ability to work independently and as part of a team
Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders
Bachelors/University degree or equivalent experience

Job Responsibility

Design, Develop and deploy generative models (e.g., GPT, GAN, VAEs) for various applications
RAG Frameworks – Able to customize and fine-tune existing RAG frameworks or design new RAG to meet project requirements
Design and Develop POC / full scale solutions on various automations in Operations area including Conversational AI, Muti Agent Systems for multiple services
Responsible for development and deployment of Machine Learning / Deep Learning based models
Collaborate with cross-functional team to understand business requirements and translate them into AI solutions
Conduct research to advance the state-of-the art in generative modeling and stay up to date with latest advancements in the field
Optimize and fine-tune models for performance, scalability, and robustness
Implement and maintain AI pipelines and infrastructure to support model training and deployment
Perform data analysis and preprocessing to ensure high-quality input for model training
Mentor junior team members and provide technical guidance

Fulltime

Data Science Team Leader

As the Senior Team Lead for Data Science - Logistics Estimations, you will be th...

Location

Germany; United Kingdom; Netherlands , Berlin; London; Amsterdam

Salary:

Not provided

Just Eat Takeaway.com

Expiration Date

Until further notice

Requirements

Proven, extensive experience in leadership and people management, with a demonstrated ability to mentor, guide, and develop Data Scientists
Prior hands-on experience developing, deploying, and maintaining machine learning models in a corporate environment
Advanced conceptual proficiency in data science and machine learning methodologies, ideally with experience in logistics, geospatial analysis, and ETA prediction or routing problems
Demonstrated experience in root-cause analysis of complex production model performance issues and the ability to translate those findings into effective business and technical solutions
Strong understanding of the model lifecycle and best practices, including testing, code reviews, and monitoring
Exceptional communication and stakeholder management skills, with the ability to influence technical peers and non-technical business leaders

Job Responsibility

Team Leadership: Mentor and manage a high-performing Data Science team, fostering a culture of speed, rigor, and courier-centric problem-solving
Strategy & Roadmap: Own the data science strategy for real-time courier pay systems, including dynamic earnings, surge pricing, and incentive programs
KPI Ownership: Drive critical business outcomes by taking ownership of key courier supply metrics like availability, acceptance rates, and earnings competitiveness
Root Cause Analysis & Communication: Diagnose structural and behavioral supply issues and translate complex economic models into actionable narratives for cross-functional stakeholders
Real-Time Architecture: Design the conceptual framework for real-time incentive engines, balancing advanced model sophistication with strict latency and reliability constraints
Dynamic Pricing: Oversee the development of predictive models that address local supply-demand imbalances and optimize real-time boost levels
Fairness & Technical Standards: Act as the senior technical guide to ensure pay models are rigorous, auditable, mathematically consistent, and free from unintended bias
MLE Collaboration: Partner closely with Machine Learning Engineers to ensure smooth, robust deployment and scaling of production-ready models
Causal Experimentation: Lead the design and execution of complex marketplace experiments to accurately measure the real-world impact of pay and incentive changes
Behavioral Modeling & ROI: Direct the modeling of courier behavior (elasticity, churn, engagement) and build frameworks to maximize the ROI of incentive spend using large-scale geospatial data

Fulltime

Data Science Lead

We are looking for a Senior / Lead Data Scientist with strong expertise in Machi...

Location

India

Salary:

Not provided

Codvo AI

Expiration Date

Until further notice

Requirements

7+ years of hands-on experience in Data Science, Machine Learning, or Applied AI roles
Strong foundation in statistical modeling and machine learning, including: Regression, boosting trees, random forests, Time-series modeling and forecasting, Optimization techniques (linear, nonlinear, stochastic)
Deep Learning expertise using frameworks such as: TensorFlow, Keras, PyTorch
Experience with RNN, LSTM, GRU, CNN is a plus
Experience with NLP and unstructured data processing
Hands-on experience with LLMs and GenAI, including: Retrieval-Augmented Generation (RAG), Vector databases (FAISS, Chroma, Pinecone, or similar), Prompt engineering and LLM evaluation, Agentic AI frameworks (e.g., LangChain, LangGraph, or similar)
Strong programming skills in Python (R is a plus)
familiarity with sh/bash scripting
Experience working with SQL and NoSQL databases
Experience building and consuming REST APIs and web services

Job Responsibility

Design, develop, and deploy production-grade ML and DL models with a focus on time-series data, forecasting, and predictive analytics
Build and optimize end-to-end ML pipelines, from data preprocessing and feature engineering to model training, evaluation, deployment, and monitoring
Apply advanced ML techniques including regression, tree-based models, ensemble methods, deep learning, and optimization algorithms
Perform feature extraction and dimensionality reduction using techniques such as autoencoders for high-dimensional datasets
Track experiments, model performance, and metrics using industry-standard tools and best practices
Design and implement LLM-powered applications, including Retrieval-Augmented Generation (RAG) systems for enterprise use cases such as analytics automation, knowledge assistants, and decision-support tools
Build document ingestion, chunking, embedding, and retrieval pipelines for structured and unstructured data using vector databases
Develop Agentic AI workflows that enable multi-step reasoning, tool usage, and autonomous task execution
Integrate LLMs with traditional ML systems to enhance explainability, insights generation, and user interaction
Implement guardrails and evaluation mechanisms to reduce hallucinations and ensure reliable, grounded LLM outputs

Fulltime

Data science associate

Join us at Barclays as a Data Science Associate. Your role sits within the Portf...

Location

United Kingdom , London

Salary:

Not provided

Barclays

Expiration Date

Until further notice

Requirements

Strong quantitative background (e.g. Mathematics, Physics, Engineering, Economics) with exposure to statistical or financial modelling through study or early experience
Proficiency in Python (e.g. pandas, NumPy) and working knowledge of SQL, with a solid understanding of data structures and data quality considerations
Basic understanding of credit risk concepts (PD, LGD, EAD, RWA), or the ability to learn them quickly
Strong problem-solving skills, with the ability to write clear, well-structured code and an interest in developing robust coding practices and production-ready solutions
Proactive, independent working style with good communication skills, and the ability to clearly explain analytical outputs and collaborate effectively

Job Responsibility

End-to-end analytical ownership from data analysis and model development to implementation and ongoing enhancement, alongside improving processes and supporting optimisation initiatives
Contribute to the evolution of the analytics platform, including the practical application of AI and advanced techniques
Support portfolio management across credit risk, balance sheet optimisation, and capital efficiency by delivering data-driven insights and analytical tools, including for SRT and transaction activity
Develop and maintain models, improve processes, and apply advanced techniques to enhance decision-making and portfolio optimisation
Partner with internal Structuring, Treasury and Middle Office to execute new structured transactions, as part of a deal team
Ongoing management and optimization of the structured transactions, partnering with internal teams and external parties such as law firms/verification agents, rating agencies and agent banks
Together with relationship/Sales teams, support with investor engagement including queries related to existing and prospective transactions
Support of wider portfolio optimization initiatives as well as analytics, returns and model enhancements

What we offer

Hybrid working (anchor days at onsite location)

Fulltime

Head of AI Data Science, Intelligence Ventures

The Head of AI Data Science serves as the head of AI research and leader of data...

Location

United States , New York

Salary:

263200.00 - 393800.00 USD / Year

Spectrum

Expiration Date

Until further notice

Requirements

Deep expertise in transformer-based sequence modeling and its application to behavioral or interaction data at consumer scale — including architecture design, training methodology, fine-tuning, and embedding quality evaluation
Proven track record developing and deploying household- or user-level embedding models applied to real-world use cases in media, marketing, commerce, and/or customer intelligence — not just research environments. Demonstrated understanding of the unique characteristics of behavioral sequence data: sparsity, temporal dynamics, multi-entity structure, and the signal differences between behavioral intent and explicit interaction
Strong command of the full data science lifecycle in production settings — from exploratory data analysis and feature engineering through model training, validation, deployment, monitoring, and iteration — at large dataset scale (billions, even trillions of records)
Hands-on proficiency with Python, PyTorch or TensorFlow, and distributed ML training frameworks
experience running ML workloads on cloud platforms (AWS SageMaker, Snowflake Cortex, Databricks, or equivalent)
Experience designing and operationalizing feature stores and predictive modeling pipelines that serve downstream intelligence products, audiences, or decision systems in production environments
Ability to communicate complex AI/ML concepts clearly to non-technical executive audiences, product stakeholders, and external partners
comfort operating as an external-facing technical spokesperson for the platform's modeling capabilities and intelligence differentiation
Track record of leading and growing high-performing data science teams
experience recruiting and developing senior ML talent in competitive markets

Job Responsibility

Direct the research, design, and training of the platform's proprietary transformer-based behavioral embedding model — a multi-entity architecture that encodes household behavior across multiple signal sources into dense, privacy-safe vector representations. Own the full model development lifecycle from architecture decisions and training methodology through validation, deployment, and ongoing iteration as new signal sources and use cases are introduced
Lead the design and build of the platform's Feature Store — translating embedding representations into interpretable, actionable behavioral signals including purchase propensities, category interest intensities, lifestyle affinities, and behavior velocity signals. Oversee the outcome anchoring methodology that trains predictive models against external third-party datasets to produce validated, commercially relevant intelligence outputs across target verticals
Partner with the Head of Technology and external development partners to ensure the AI/ML architecture is production-grade, built for household-scale throughput, and integrated cleanly into the platform's cloud-native infrastructure. Establish model evaluation frameworks, quality benchmarks, and MLOps practices that enforce a strong bias toward production-deployed, commercially validated outputs — not just research-quality results
Serve as the platform's primary AI research voice in external partner conversations — including technical engagements with cloud AI platforms, frontier model teams, and enterprise data partners — articulating the platform's embedding architecture, signal differentiation, and model enrichment value proposition to sophisticated technical counterparts. Contribute to the development of packaged intelligence products such as behavioral demand indices, persona clusters, and predictive propensity scores
Establish the platform's responsible AI framework — including bias testing protocols for behavioral embeddings, model documentation standards, and privacy-preserving ML techniques — ensuring all intelligence products meet ethical and regulatory standards for consumer behavioral data
Build and lead a team of data scientists and ML researchers capable of competing with talent from the world's leading AI research and applied ML organizations. Establish the team's research agenda, hiring priorities, and culture of rigorous experimentation — maintaining a clear bias toward applied, production-oriented work while preserving the intellectual ambition required to stay ahead of a rapidly evolving AI landscape

Fulltime

Senior Manager, Data Science - Inventory Capabilities

We’re looking for a Sr. Manager Data Scientist to join Walmart’s Inventory Capab...

Location

United States , Bentonville

Salary:

110000.00 - 220000.00 USD / Year

Walmart

Expiration Date

Until further notice

Requirements

Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 5 years' experience in an analytics related field. Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 3 years' experience in an analytics related field. Option 3: 7 years' experience in an analytics or related field.
Strong expertise in applied data science and advanced analytics (optimization, forecasting, simulation, machine learning)
Comfortability operating at both strategic and technical depth — from building models to influencing enterprise priorities
Experience simplifying complex science for senior leaders and business partners to drive adoption and decision-making
Ability to excel in ambiguous, high-pressure environments with multiple stakeholders and competing priorities
A passion for solving global-scale inventory optimization challenges that directly improve customer experience
MBA, MS, or PhD in Operations Research, Industrial Engineering, Applied Mathematics, Statistics, Operations Management, or Supply Chain Management
Experience implementing data science models using modern programming languages (e.g., Python, PySpark, R) and using large dataset mining tools (e.g., SQL)
Experience with advanced analytical modeling tools, network optimization, and long-range planning
Extensive experience or knowledge of eCommerce, retail and supply chain domains and related information systems — search, impressions, conversion, distribution/transportation, sourcing, planning, manufacturing, and inventory management

Job Responsibility

Applied Research: Analyze large-scale datasets to refine model inputs (forecasts, cost structures, constraints) and improve accuracy and scalability
Model Development: Design and deliver new models and algorithms across forecasting, placement, inventory targets, safety stock, and exits to optimize cost, speed, and customer experience
Optimization & Machine Learning: Apply advanced techniques in optimization, probabilistic forecasting, and ML to improve efficiency and resilience across Walmart's global network
End-to-End Execution: Lead projects from framing business problems, to building and validating models, to partnering with engineering for deployment
Collaboration & Influence: Translate science into strategy, working with Merchandising, Supply Chain, and Tech partners to align on priorities and deliver measurable impact
Future-Focused Capabilities: Advance scenario modeling, dynamic inventory placement, multi-echelon optimization, and placement orchestration for omni-channel fulfillment

What we offer

competitive pay
performance-based bonus awards
medical
vision
dental
401(k)
stock purchase
company-paid life insurance
PTO (including sick leave)
parental leave

Fulltime

Select Country

Data Science - Structures

Job Description

Job Responsibility

Requirements

Nice to have

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