101 to 125 of 140 Remote Model Validation Jobs

be doing: Spotting opportunities to apply data science and ML to solve meaningful product challenges Designing and building data pipelines to support experimentation and model development Running structured experiments and analysing performance metrics to validate outcomes Deploying and maintaining models in production and delivering clear business impact Collaborating closely … engineering, and business stakeholders What you’ll bring: Hands-on experience developing and productionising ML models Strong analytical background with applied stats, EDA, and model validation techniques Confidence working with structured data pipelines and modern tooling (AWS, Snowflake, Airflow, DBT) Curiosity for emerging techniques and an eagerness to More ❯

be doing: Spotting opportunities to apply data science and ML to solve meaningful product challenges Designing and building data pipelines to support experimentation and model development Running structured experiments and analysing performance metrics to validate outcomes Deploying and maintaining models in production and delivering clear business impact Collaborating closely … engineering, and business stakeholders What you’ll bring: Hands-on experience developing and productionising ML models Strong analytical background with applied stats, EDA, and model validation techniques Confidence working with structured data pipelines and modern tooling (AWS, Snowflake, Airflow, DBT) Curiosity for emerging techniques and an eagerness to More ❯

experience with classical ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database More ❯

experience with classical ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database More ❯

utilised across all of TradingHub's product offerings Research and development of broad models of market dynamics across multiple asset classes Prototyping, testing, and validation of TradingHub's proprietary mathematical/statistical models Use of in-house big data language for the large-scale pricing and analysis of security More ❯

utilised across all of TradingHub’s product offerings Research and development of broad models of market dynamics across multiple asset classes Prototyping, testing, and validation of TradingHub’s proprietary mathematical/statistical models Use of in-house big data language for the large-scale pricing and analysis of security More ❯

performance 2. Data Management and Preparation Work with Data Engineers to prepare clean, reliable datasets for analysis and modeling Implement data quality checks and validation procedures Perform feature engineering to optimize model performance Develop efficient data processing pipelines for model training and deployment Document data transformations and … analytical frameworks and approaches Build productive relationships with stakeholders to understand their needs and requirements Provide data-driven insights to support strategic decision-making Model organizational values and lead by example for more junior team members 4. Insight Communication and Visualization Build clear visualizations and communicate findings to technical … and non-technical stakeholders Present complex analytical results in accessible and actionable formats Develop dashboards and reports to track model performance and business impact Create compelling narratives around data insights to drive organizational change Document methodologies and findings for knowledge sharing 5. Continuous Improvement and Innovation Research and implement More ❯

role requires some knowledge of Price Risk processes covering inventory, valuations, front-to-back controls, market risk processes (Value-at-Risk VaR, Stress-testing), model risk (including model methodology and validation), product control (P&L explain), IPV (Independent Price Verification) and end to end governance. Successful execution … in class target state, as well as a set of practical actions to achieve. The workstreams range from frameworks with a focus on operating model, risk and controls, methodologies, data and data controls, front office valuations and controls, various market risk related workstreams to P&L attribution analysis (PAA … issues and collaborate with stakeholders to generate solutions Work with Control and Internal Audit stakeholders to ensure credible challenge throughout the remediation process and validation of results in line with Citi's Internal Audit requirements Present on status of the program to senior stakeholders within Citi Qualifications & skills More ❯

competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard More ❯

competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard More ❯

STEP (STIBO) MDM hands-on development experience. Strong knowledge in STEP's (STIBO) capabilities in terms of data ingestion, data modeling, data quality/validation, match/merge process and logic, MDM tool internal workflows, and data syndication from MDM tool. Strong communication skills, both verbal and written. Proven More ❯

CAPE). • Liaise with Naval Sea Systems Command (NAVSEA) Cost Engineering office (SEA 05C) for ship cost estimating and refinement of Navy Force Affordability Model or similar model for shipbuilding plan cost estimates. Action Officer (AO)/Requirements Officer (RO) Support • Coordinate and provide responses and information to … to the Navy SCN Appropriation Manager for alternative shipbuilding option development and for long-range shipbuilding planning. • Provide research, analysis, and liaise with shipbuilding model managers in validating the battle force inventory spreadsheets and databases. • Assist in the preparation, review, and analysis of appropriate instructions and other directives covering More ❯

AI projects using PyTorch Craft and refine ML models to improve their performance, scalability, and adaptability Analyze and interpret complicated data sets to advise model development and ensure accuracy and efficiency Stay abreast of the latest developments in ML and artificial intelligence, integrating new methodologies and techniques as appropriate … Collaborate across multiple teams to integrate ML solutions within broader product and platform initiatives Contribute to the development of standardized processes for model evaluation, validation, and deployment to production environments Drive the exploration and adoption of brand new ML technologies to maintain our competitive edge in the industry. … Experience in applying machine learning to domains such as e-commerce, finance, health care, etc Experience in using ML tools such as Mlflow for model lifecycle management Experience with common ML libraries and frameworks such as TensorFlow, PyTorch, Keras, Scikit-learn Familiarity with timely engineering and large language models More ❯

to support generative AI workflows and large language models (LLMs) in production. What You’ll Do Design, build, and maintain scalable ML platforms for model development, experimentation, and production workflows. Automate ML infrastructure deployment, including data pipelines, model training, validation, and deployment. Manage the full ML lifecycle … from model versioning to deployment, monitoring, and retraining. Optimise large language model (LLM) operations , ensuring efficient fine-tuning, deployment, and performance monitoring. Collaborate closely with data scientists and engineers to develop and deploy ML models at scale. Optimise performance for inference and training across GPUs and cloud-based … handling sensitive data. Evaluate and integrate MLOps tools (MLflow, Kubeflow, etc.) to enhance efficiency. Implement monitoring and alerting systems to detect anomalies and maintain model reliability. What We’re Looking For 3+ years of experience in software engineering, infrastructure, or MLOps roles. Proven expertise in building and maintaining ML More ❯

to support generative AI workflows and large language models (LLMs) in production. What You’ll Do Design, build, and maintain scalable ML platforms for model development, experimentation, and production workflows. Automate ML infrastructure deployment, including data pipelines, model training, validation, and deployment. Manage the full ML lifecycle … from model versioning to deployment, monitoring, and retraining. Optimise large language model (LLM) operations , ensuring efficient fine-tuning, deployment, and performance monitoring. Collaborate closely with data scientists and engineers to develop and deploy ML models at scale. Optimise performance for inference and training across GPUs and cloud-based … handling sensitive data. Evaluate and integrate MLOps tools (MLflow, Kubeflow, etc.) to enhance efficiency. Implement monitoring and alerting systems to detect anomalies and maintain model reliability. What We’re Looking For 3+ years of experience in software engineering, infrastructure, or MLOps roles. Proven expertise in building and maintaining ML More ❯

machine learning, regulatory subject matter expert, customer, and software engineering teams. Set the short, mid, and long-term strategy for our AI products. Oversee model training, validation, and testing processes, by establishing rigorous reviewer guidelines, recruiting the appropriate domain experts, and determining sampling strategy/size. Deeply understand More ❯

Proven experience in operational resilience, risk management, or business continuity within financial services or a regulated environment. Strong knowledge of AI/ML technologies, model governance, and responsible AI principles. Understanding of machine learning models, natural language processing (NLP), and AI algorithms. Knowledge of model lifecycle: training, validation, deployment, and monitoring. Ability to assess and mitigate risks from AI systems (e.g., model drift, bias, explainability). Familiarity with resilience frameworks and regulatory expectations (e.g., PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring More ❯

Proven experience in operational resilience, risk management, or business continuity within financial services or a regulated environment. Strong knowledge of AI/ML technologies, model governance, and responsible AI principles. Understanding of machine learning models, natural language processing (NLP), and AI algorithms. Knowledge of model lifecycle: training, validation, deployment, and monitoring. Ability to assess and mitigate risks from AI systems (e.g., model drift, bias, explainability). Familiarity with resilience frameworks and regulatory expectations (e.g., PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring More ❯

Proven experience in operational resilience, risk management, or business continuity within financial services or a regulated environment. Strong knowledge of AI/ML technologies, model governance, and responsible AI principles. Understanding of machine learning models, natural language processing (NLP), and AI algorithms. Knowledge of model lifecycle: training, validation, deployment, and monitoring. Ability to assess and mitigate risks from AI systems (e.g., model drift, bias, explainability). Familiarity with resilience frameworks and regulatory expectations (e.g., PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring More ❯

Proven experience in operational resilience, risk management, or business continuity within financial services or a regulated environment. Strong knowledge of AI/ML technologies, model governance, and responsible AI principles. Understanding of machine learning models, natural language processing (NLP), and AI algorithms. Knowledge of model lifecycle: training, validation, deployment, and monitoring. Ability to assess and mitigate risks from AI systems (e.g., model drift, bias, explainability). Familiarity with resilience frameworks and regulatory expectations (e.g., PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring More ❯

Proven experience in operational resilience, risk management, or business continuity within financial services or a regulated environment. Strong knowledge of AI/ML technologies, model governance, and responsible AI principles. Understanding of machine learning models, natural language processing (NLP), and AI algorithms. Knowledge of model lifecycle: training, validation, deployment, and monitoring. Ability to assess and mitigate risks from AI systems (e.g., model drift, bias, explainability). Familiarity with resilience frameworks and regulatory expectations (e.g., PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring More ❯

Proven experience in operational resilience, risk management, or business continuity within financial services or a regulated environment. Strong knowledge of AI/ML technologies, model governance, and responsible AI principles. Understanding of machine learning models, natural language processing (NLP), and AI algorithms. Knowledge of model lifecycle: training, validation, deployment, and monitoring. Ability to assess and mitigate risks from AI systems (e.g., model drift, bias, explainability). Familiarity with resilience frameworks and regulatory expectations (e.g., PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring More ❯

Lead the development of credit risk models and play a key role in our integration strategy. Collaborative Environment : Work closely with Head of Credit Model Integration and the talented team across different departments. Become a catalyst for Credit Modelling change : Developing Group Models and new ways of working for … a Group Modelling function. Transform model risk practice: Help embed improvements in model risk management across the Group. If youre ready to help shape the future of credit risk modelling at Coventry Building Society, we want to hear from you! Find out more about the fantastic benefits of … About you We are looking for someone with the experience and ability to develop new models and contribute to driving forward improvements in credit model delivery practice across the Group. To be successful in this role, it's essential that you're able to demonstrate: Highly proficient in programming More ❯

partner to women worldwide. The Job What you'll be doing You'll be responsible for: Leading GenAI technical strategy and implementation, including: Developing model benchmarking and evaluation frameworks Driving AI tooling development and integration Designing human-in-the-loop systems Establishing dataset collection and curation methodologies Architecting AI … infrastructure (model serving, monitoring, scaling) Optimizing performance and defining quality metrics Providing cross-functional leadership: Partnering with medical teams for model safety validation Collaborating with product teams to define model usefulness metrics and user needs Working alongside product peers on solution design and user experience Making More ❯

partner to women worldwide. The Job What you'll be doing You'll be responsible for: Leading GenAI technical strategy and implementation, including: Developing model benchmarking and evaluation frameworks Driving AI tooling development and integration Designing human-in-the-loop systems Establishing dataset collection and curation methodologies Architecting AI … infrastructure (model serving, monitoring, scaling) Optimizing performance and defining quality metrics Providing cross-functional leadership: Partnering with medical teams for model safety validation Collaborating with product teams to define model usefulness metrics and user needs Working alongside product peers on solution design and user experience Making More ❯