Manager, Data Science - Agentic AI

VP - Payment Analytics

Manager, Quality Assurance - Digital Analytics (L09)

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Director, Data Science

Senior Manager Data Science

Manager, Data Science (GNS)

Senior Data Scientist II

Responsibilities: <ul><li>Leading development of advanced AI Agentic tech.</li><li>Working closely with other data scientists and engineers to design, develop, and deploy AI solutions.</li><li>Collaborating with cross-functional teams to ensure AI solutions are aligned with business goals and customer needs.</li><li>Analyzing large datasets to identify patterns and trends that can inform AI development.</li><li>Communicating findings and insights to stakeholders in a clear and concise manner.</li><li>Staying current with the latest AI and machine learning trends and technologies.</li><li>Mentoring junior data scientists and provide guidance on AI and machine learning best practices</li><li>Driving best practices across our data science functions.</li><li>Setting strategy and team direction, with a clear vision for Data Science.</li><li>Working with product leaders to apply data science solutions..</li><li>Evaluating and creating new frameworks and defining methodologies and governance.</li><li>Leading small- to medium-sized teams (direct or indirect).</li><li>Setting a high bar for model development.</li></ul> Requirements: <ul><li>3+ years of people management experience.</li><li>7+ years of AI/ML experience.</li><li>Significant experience in managing larger technical teams (direct or indirect).</li><li>Have experience in mentoring or training others, acting as a subject matter expert to guide colleagues.</li><li>Be a natural problem solver, able to take a lead in collaborating to resolve issues.</li><li>Have excellent communication skills and the ability to convey information clearly to senior stakeholders.</li><li>Deep understanding of machine learning algorithms, including deep learning, gradient boosting, and random forests.</li><li>Experience working directly with large language models and Transformer based architectures including BERT, RoBERTa, T5 etc.</li><li>Working knowledge of applying recent LLMs including ChatGPT, GPT 3.5, OPT, BLOOM, etc.</li><li>Experience with reinforcement learning, prompt engineering, hallucination mitigation.</li><li>Working understanding of the business risks associated with applying LLM in a business.</li><li>Experience working with large datasets and distributed computing systems (e.g., Hadoop, Spark).</li><li>Strong coding skills in Python or another programming language.</li><li>Excellent communicator, with the ability to translate complex technical concepts to non-technical stakeholders.</li></ul>

Responsibilities: <ul><li>Develop and execute a clear, actionable, and time-bound analytics roadmap, outlining key milestones, resources, and timelines to achieve payment analytics objectives.</li><li>Lead the design and delivery of comprehensive analytics across all payment channels, products, and features, providing end-to-end visibility into the payment lifecycle.</li><li>Identify, assess, and bridge data gaps by collaborating with business, technology, operations, and data engineering teams to ensure complete, accurate, and timely payment data capture.</li><li>Translate complex payment data into actionable insights and strategic recommendations that optimize the payment process, improve customer experience, and drive business growth.</li><li>Partner closely with Tech &amp; Ops Payments, Product Analytics, Payment Train, Customer Experience, and other key stakeholders to embed analytics into decision-making at all levels.</li><li>Build, mentor, and manage a high-performing analytics team, fostering a culture of innovation, accountability, and continuous learning.</li><li>Cultivate and maintain strong relationships across teams to effectively communicate analytics value and drive enterprise-wide adoption.</li><li>Support regulatory review and governance</li><li>Promote advanced analytics practices, including proof-of-concept initiatives, to keep the organization at the forefront of payment analytics capabilities.</li><li>Lead internal initiatives, perform other duties and/or special projects, as assigned</li></ul> Requirements: <ul><li>Degree in Statistics, Mathematics, Economics, Engineering, or any other quantitative streams with 10+ years of hands-on relevant analytics/data science experience, or in lieu of a degree 12+ years of relevant analytics/data science experience.</li><li>8+ years of hands-on Experience in SQL /SAS with proven ability to leverage advanced algorithms and be efficient in handling complex/large data;</li><li>3+ years of experience in BI tools, e.g. Tableau etc.</li><li>5+ years of experience in managing teams with ability to motivate team, drive change, create a culture of learning, innovation and recognition</li><li>Ability to solve complex technical and people-oriented problems with minimal supervision</li><li>Advanced proficiency in MS-Excel and MS-PowerPoint with superior data analyses and story boarding skills</li><li>Strong written/oral communication &amp; stakeholder management skills</li></ul>

Responsibilities: <ul><li>Lead, manage and setup tagging and cookies auditing tools for automating manually tested scenarios and user journeys into automated scripts/flows across multiple our client platforms.</li><li>Perform code audits on tag management systems for feature enhancements, run regression testing</li><li>Provide QA requirements, review &amp; validate implementation of analytical, marketing technologies, across our client digital platforms via tag management systems</li><li>Mentor and guide team members on QA tools and processes</li></ul> Requirements: <ul><li>2 to 5 years of hands-on experience on any one of the tools to set up automation like Observepoint across consumer-facing websites, identify &amp; report out test results</li><li>1 to 3 years of hands-on experience in JavaScript, HTML, CSS or web design with ability to independently deliver on basic JavaScript concepts, set up algorithms/define flows for quality analyst role (QA)</li><li>1 to 3 years working experience/knowledge in Tag management tools like Tealium, Adobe Launch, or Google Tag Manager</li><li>Working knowledge &amp; understanding of core concepts / metrics in retail finance</li><li>Problem solving mindset &amp; Self-starter</li></ul>

Responsibilities: <ul><li>Ability to troubleshoot problems in a live environment and provide real time help with critical issues.</li><li>Write code that is easily understood and maintainable by other team members.</li><li>Keep up to date with developments in technologies we are using.</li><li>Ability to communicate and work effectively, supporting various business departments.</li><li>Ability to work in fast-paced interdisciplinary environment.</li></ul> Requirements <ul><li>BA/BS in Computer Science or equivalent practical experience.&nbsp;</li><li>At least 3+ years of post-university experience as a full-stack engineer.</li><li>Solid knowledge of programming fundamentals—algorithms, data structures, design patterns, and paradigms.</li><li>Strong knowledge of Python.</li><li>Solid knowledge SQL and relational databases.</li><li>Knowledge of Extract-Transform-Load (ETL) and big data analytics tools.</li><li>Experience working in a cloud environment (AWS, Azure, GCP).</li><li>Familiarity with popular Python data analytics frameworks.</li><li>Familiarity with Snowflake or other large analytics engines.</li><li>Experience with data warehousing and working in environments with large scale, complex analytics requirements.</li><li>Familiarity with DAG-based job scheduling tools.</li><li>Cloud-native container orchestration platforms and tools.</li><li>Experience working in the Financial Services industry.</li></ul>

Responsibilities: <ul><li>Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.&nbsp;</li><li>Build and scale ML pipelines using Vertex AI Pipelines, Kubeflow, Airflow, and manage infra-as-code with Terraform/Helm.&nbsp;</li><li>Implement automated retraining, drift detection, and re-deployment of ML models.&nbsp;</li><li>Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.&nbsp;</li><li>Implement model monitoring, observability, and alerting across accuracy, latency, and cost.&nbsp;</li><li>Integrate and manage feature stores, knowledge graphs, and vector databases for advanced ML/RAG use cases.&nbsp;</li><li>Ensure pipelines are secure, compliant, and cost-optimized.&nbsp;&nbsp;</li><li>Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance .&nbsp;</li><li>Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.&nbsp;</li><li>Stay current with the latest ML research and apply new tools pragmatically to production systems.&nbsp;</li><li>Collaborate with product managers, DS, and engineers to translate business problems into reliable ML systems.&nbsp;</li></ul> Requirements: <ul><li>Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).&nbsp;</li><li>5+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.&nbsp;</li><li>Strong foundations in data structures, algorithms, and distributed systems.&nbsp;</li><li>Proficient in Python (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.&nbsp;</li><li>Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).&nbsp;</li><li>Experience with containerization (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).&nbsp;</li><li>Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform/Helm), and configuration management.&nbsp;</li><li>Experience with big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).&nbsp;</li><li>Practical exposure to model observability tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)&nbsp;</li><li>Strong understanding of statistical methods, ML algorithms, and deep learning architectures.&nbsp;</li></ul>

Responsibilities: Strategic Leadership:&nbsp; <ul><li>Define and execute the data science vision and roadmap aligned with business objectives and technological advancement opportunities.</li></ul> Team Management:&nbsp; <ul><li>Build, lead, and mentor a high-performing team of data scientists, fostering a culture of innovation, collaboration, and continuous learning.</li></ul> Advanced Research Direction:&nbsp; <ul><li>Direct cutting-edge research initiatives in NLP, LLMs, and other emerging AI technologies to maintain competitive advantage.</li></ul> Machine Learning and AI Solutions:&nbsp; <ul><li>Lead the development and implementation of machine learning algorithms and AI solutions to solve complex business problems.</li></ul> Data Collection and Preparation:&nbsp; <ul><li>Establish protocols for collecting, cleaning, and preprocessing large datasets, ensuring data quality and reliability.</li></ul> Data Visualization Strategy:&nbsp; <ul><li>Guide the creation of informative and compelling data visualizations to communicate results and insights to stakeholders effectively.</li></ul> Cross-functional Collaboration:&nbsp; <ul><li>Partner with executive leadership and cross-functional teams to identify strategic opportunities and address business challenges.</li></ul> Model Deployment and MLOps:&nbsp; <ul><li>Oversee the deployment of machine learning models into pro duction environments, ensuring scalability and reliability.</li></ul> Documentation Standards:&nbsp; <ul><li>Establish comprehensive documentation standards for projects, models, and code for knowledge sharing and reproducibility.</li></ul> Budget and Resource Management:&nbsp; <ul><li>Manage departmental budget, resource allocation, and infrastructure needs for data science operations.</li></ul> Ethical AI Governance:&nbsp; <ul><li>Develop and enforce ethical guidelines and best practices for AI development and deployment.</li></ul> Requirements: <ul><li>Bachelors, Masters or Ph.D. in Data Science, Computer Science, Statistics, or a related field; MBA or additional business education is a plus.</li><li>10+ years of progressive experience in data science, machine learning, or AI, with at least 8 years in leadership positions.</li><li>Demonstrated experience in managing and scaling data science teams of 15+ professionals.</li><li>Proven record of delivering high-impact AI and ML solutions that have driven significant business value.</li><li>Deep expertise with generative AI models and techniques (e.g., LLMs, GANs) for content generation and their practical applications.</li><li>Advanced knowledge of statistical analysis, machine learning algorithms, and data manipulation techniques at enterprise scale.</li><li>Experience in setting technical direction and implementing MLOps practices for model deployment and monitoring.</li><li>Strong business acumen with the ability to translate complex technical concepts into business value.</li><li>Excellent communication and leadership skills, with experience presenting to executive leadership.</li><li>Experience working in a global or multicultural environment.</li><li>Record of successful collaboration with product, engineering, and business teams.</li><li>Proficiency in multiple programming languages relevant to data science (Python, R, etc.) and big data technologies.</li></ul>

Responsibilities: <ul><li>Strategic Leadership: Define and execute the data science vision and roadmap aligned with business objectives and technological advancement opportunities.</li><li>Team Management: Build, lead, and mentor a high-performing team of data scientists, fostering a culture of innovation, collaboration, and continuous learning.</li><li>Advanced Research Direction: Direct cutting-edge research initiatives in NLP, LLMs, and other emerging AI technologies to maintain competitive advantage.</li><li>Machine Learning and AI Solutions: Lead the development and implementation of machine learning algorithms and AI solutions to solve complex business problems.</li><li>Data Collection and Preparation: Establish protocols for collecting, cleaning, and preprocessing large datasets, ensuring data quality and reliability.</li><li>Data Visualization Strategy: Guide the creation of informative and compelling data visualizations to communicate results and insights to stakeholders effectively.</li><li>Cross-functional Collaboration: Partner with executive leadership and cross-functional teams to identify strategic opportunities and address business challenges.</li><li>Model Deployment and MLOps: Oversee the deployment of machine learning models into pro duction environments, ensuring scalability and reliability.</li><li>Documentation Standards: Establish comprehensive documentation standards for projects, models, and code for knowledge sharing and reproducibility.</li><li>Budget and Resource Management: Manage departmental budget, resource allocation, and infrastructure needs for data science operations.</li><li>Ethical AI Governance: Develop and enforce ethical guidelines and best practices for AI development and deployment.</li></ul> Requirements: <ul><li>Bachelors, Masters or Ph.D. in Data Science, Computer Science, Statistics, or a related field; MBA or additional business education is a plus.</li><li>10+ years of progressive experience in data science, machine learning, or AI, with at least 8 years in leadership positions.</li><li>Demonstrated experience in managing and scaling data science teams of 15+ professionals.</li><li>Proven record of delivering high-impact AI and ML solutions that have driven significant business value.</li><li>Deep expertise with generative AI models and techniques (e.g., LLMs, GANs) for content generation and their practical applications.</li><li>Advanced knowledge of statistical analysis, machine learning algorithms, and data manipulation techniques at enterprise scale.</li><li>Experience in setting technical direction and implementing MLOps practices for model deployment and monitoring.</li><li>Strong business acumen with the ability to translate complex technical concepts into business value.</li><li>Excellent communication and leadership skills, with experience presenting to executive leadership.</li><li>Experience working in a global or multicultural environment.</li><li>Record of successful collaboration with product, engineering, and business teams.</li><li>Proficiency in multiple programming languages relevant to data science (Python, R, etc.) and big data technologies.</li></ul>

Responsibilities: <ul><li>Manage a team of data scientists and engineers to design, develop, and deploy advanced AI solutions</li><li>Lead the development of AI and machine learning models to solve business problems</li><li>Research, build, and deploy models based on shallow and deep machine learning</li><li>Train robust NLP-based models on large datasets</li><li>Collaborate with cross-functional teams to align AI solutions with business goals and customer needs</li><li>Design APIs for disseminating data science outputs</li><li>Oversee the scaling of production systems to meet growing demands</li></ul> Requirements: <ul><li>Deep understanding of machine learning algorithms, including deep learning, gradient boosting, and random forests.</li><li>Experience working directly with large language models and Transformer based architectures including BERT, RoBERTa, T5 etc.</li><li>Working knowledge of applying recent LLMs including ChatGPT, GPT 3.5, OPT, BLOOM, etc.</li><li>Experience with reinforcement learning, prompt engineering, hallucination mitigation.</li><li>Working understanding of the business risks associated with applying LLM in a business.</li><li>Experience working with large datasets and distributed computing systems (e.g., Hadoop, Spark).</li><li>Strong coding skills in Python or another programming language.</li><li>Excellent communicator, with the ability to translate complex technical concepts to non-technical stakeholders.</li><li>Strong problem-solving skills and the ability to think outside the box. </li></ul>

Responsibilities: <ul><li>Manage a team of data scientists and engineers to design, develop, and deploy advanced AI solutions.</li><li>Lead the development of AI and machine learning models to solve business problems.</li><li>Research, build, and deploy models based on shallow and deep machine learning.</li><li>Train robust NLP-based models on large datasets.</li><li>Collaborate with cross-functional teams to align AI solutions with business goals and customer needs.</li><li>Design APIs for disseminating data science outputs.</li><li>Oversee the scaling of production systems to meet growing demands.</li><li>Working closely with other data scientists and engineers to design, develop, and deploy AI solutions.</li><li>Analyzing large datasets to identify patterns and trends that can inform AI development.</li><li>Communicating findings and insights to stakeholders in a clear and concise manner.</li><li>Staying current with the latest AI and machine learning trends and technologies.</li><li>Mentoring junior data scientists and provide guidance on AI and machine learning best practices.</li><li>Driving best practices across our data science functions.</li><li>Setting strategy and team direction, with a clear vision for Data Science.</li><li>Working with product leaders to apply data science solutions.</li><li>Evaluating and creating new frameworks and defining methodologies and governance.</li><li>Leading small- to medium-sized teams (direct or indirect).</li><li>Setting a high-bar for model development.</li></ul> Requirements: <ul><li>Have substantial experience in a senior data scientist role.</li><li>Have experience in leading complex projects and customer relationships.</li><li>Have significant experience in managing larger Technical teams (direct or indirect).</li><li>Have experience in mentoring or training others, acting as a subject matter expert to guide colleagues.</li><li>Be a natural problem solver, able to take a lead in collaborating to resolve issues.</li><li>Have excellent communication skills and the ability to convey information clearly to senior stakeholders.</li></ul> Highly Desired Skills and Experience: <ul><li>PhD or Masters degree in Computer Science, Mathematics, Statistics, or a related field.</li><li>7-10+ years of experience in AI and machine learning.</li><li>Deep understanding of machine learning algorithms, including deep learning, gradient boosting, and random forests.</li><li>Experience working directly with large language models and Transformer based architectures including BERT, RoBERTa, T5 etc.</li><li>Working knowledge of applying recent LLMs including ChatGPT, GPT 3.5, OPT, BLOOM, etc.</li><li>Experience with reinforcement learning, prompt engineering, hallucination mitigation.</li><li>Working understanding of the business risks associated with applying LLM in a business.</li><li>Experience working with large datasets and distributed computing systems (e.g., Hadoop, Spark).</li><li>Strong coding skills in Python or another programming language.</li><li>Excellent communicator, with the ability to translate complex technical concepts to non-technical stakeholders.</li><li>Strong problem-solving skills and the ability to think outside the box. </li></ul>

About the Role : <ul><li>As a data scientist on our team, you will work on new product development in a small team environment writing production code in both run-time and build-time environments.&nbsp;</li><li>You will help propose and build data-driven solutions for high-value customer problems by discovering, extracting, and modeling knowledge from large-scale natural language datasets including matter and contract repository, invoice/legal spend data and work management.&nbsp;</li><li>You will prototype new ideas, collaborating with other data scientists as well as product designers, data engineers, front-end developers, and a team of expert legal data annotators.</li><li>You will get the experience of working in a start-up culture with the large datasets and many other resources of an established company.</li><li>We are committed to advancing gender diversity within the technology sector and supporting greater representation of women in tech. To further this commitment,</li></ul> Responsibilities: <ul><li>Develop and implement LLM-based applications tailored for legal use-cases&nbsp;</li><li>Evaluate and help maintain our data assets and training/evaluation data sets&nbsp;</li><li>Design and build pipelines for preprocessing, annotating, and managing legal document datasets&nbsp;</li><li>Collaborate with legal experts to understand requirements and ensure ML-applications meet domain-specific needs&nbsp;</li><li>Conduct experiments and evaluate model performance to drive continuous improvements&nbsp;</li><li>Fine-tune and deploy large language models to enhance their performance on legal text processing tasks&nbsp;</li><li>Interface with other technical personnel or team members to finalize requirements.&nbsp;</li><li>Work closely with other development team members to understand complex product requirements and translate them into software designs.&nbsp;</li><li>Successfully implement development processes, coding best practices, and code reviews for production environments.</li></ul> Requirements:&nbsp; <ul><li>Able to work in person 2 days per week&nbsp;</li><li>Bilingual English and Spanish&nbsp;</li><li>Practical experience with large language models, prompt engineering, fine-tuning RAG-based applications and benchmarking, using frameworks such as LangChain&nbsp;</li><li>MS in Data Science, Computer Science, Statistics, Machine Learning, or relevant work experience&nbsp;</li><li>Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, Flair, BERT (ways to vectorize text data)&nbsp;</li><li>Formal training in machine learning: dimensionality reduction, clustering, embeddings, and sequence classification algorithms&nbsp;</li><li>Strong Python background&nbsp;</li><li>Knowledge of AWS, GCP, Azure, or other cloud platform&nbsp;</li><li>Understanding of data modeling principles and complex data models.&nbsp;</li><li>Proficiency with relational and NoSQL databases as well as vector stores (e.g., MySQL, Postgres, Solr, Elasticsearch/OpenSearch)</li><li>Knowledge of Spark-Scala, Ray or other distributed computing systems highly preferred&nbsp;</li><li>Knowledge of API development, containerization, and machine learning deployment highly preferred&nbsp;</li><li>Experience with ML Ops/AI Ops including experiment tracking tools like LangFuse and DVC&nbsp;</li><li>Experience with deep learning frameworks such as PyTorch, Tensorflow and Hugging Face Transformers Work in a way that works for you We promote a healthy work/life balance across the organization. </li><li>We offer an appealing working prospect for our people.&nbsp;</li><li>With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate. </li><li>Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.</li></ul>

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12 algorithms Jobs found

Manager, Data Science - Agentic AI

VP - Payment Analytics

Manager, Quality Assurance - Digital Analytics (L09)

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Director, Data Science

Director, Data Science

Senior Manager Data Science

Manager, Data Science (GNS)

Senior Data Scientist II

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12 algorithms Jobs found

Manager, Data Science - Agentic AI

VP - Payment Analytics

Manager, Quality Assurance - Digital Analytics (L09)

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Director, Data Science

Director, Data Science

Senior Manager Data Science

Manager, Data Science (GNS)

Senior Data Scientist II

WhiteCrow WhiteCrow is on a mission to connect companies with the best talent across the globe.

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