Duties: Lead, manage and mentor a team of machine learning engineers and applied scientists; Collaborate with product managers to translate business and product requirements into AI projects; Collaborate with fellow technical leaders to ensure the successful and timely delivery of models and integration of services; Coordinate with global teams to drive projects from research to production; Develop new OCI services and features bringing to bear recent advances in generative AI, machine learning and deep learning; Design and review the architecture of generative AI, including data, model, training, and evaluation, employing standard processes; Participate in project planning, review, and retrospective sessions
Requirements: Bachelor's or Master's Degree or equivalent experience in Computer Science or Sciences; PhD or equivalent experience in the relevant area will be a plus; 10+ years overall experience, with at least 2 years in people manager role; Demonstrated experience in designing and implementing scalable AI models for production; Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers; Practical experience with the latest technologies in LLM and generative AI, such as parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering techniques like Tree-of-Thoughts

Duties: Collaborate with stakeholders and data teams to identify business challenges amenable to machine learning solutions; Perform exploratory data analysis to uncover insights and transform business questions into concrete modeling tasks; Develop, optimize, and enhance machine learning algorithms and neural network architectures for natural language processing, computer vision, forecasting, and other domains; Build reliable end-to-end ML pipelines for data processing, model training, evaluation, and deployment; Continuously monitor, retrain, and update models to maintain predictive accuracy; Operationalize and serve models at scale using containers, orchestration, and cloud infrastructure
Requirements: Have a B.Tech/M.Tech or equivalent in Computer Science, Statistics, Mathematics, or related quantitative field or equivalent experience; Have 8+ years of professional experience developing and deploying machine learning models and pipelines at scale; Self-starter who can take initiative, drive consensus, and mentor junior team members; Strong software engineering skills and experience developing in languages like Python, R, and Scala; Experience with statistical and machine learning theory, algorithms, and frameworks like SciKit-Learn, PyTorch, TensorFlow

Duties: Develop new OCI services and features leveraging recent advances in generative AI, machine learning and deep learning; Evaluate and benchmark AI models including LLMs, perform inference testing, optimise models and monitor model performance in production; Collaborate with fellow technical leaders to ensure the successful and timely delivery of models and integration of services; Contribute to the architecture of generative AI, including data, model, training, and evaluation, employing best practices; Develop production code and advocate for the best coding and engineering practices; Participate in project planning, review, and retrospective sessions
Requirements: Demonstrated experience in designing and implementing scalable AI models for production; Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers; Practical experience with the latest technologies in LLM and generative AI, such as parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering techniques like Tree-of-Thoughts; Hands-on experience with emerging LLM frameworks and plugins, such as LangChain, LlamaIndex, VectorStores and Retrievers, LLM Cache, LLMOps (MLFlow), LMQL, Guidance, etc

Duties: Develop new OCI services and features leveraging recent advances in generative AI, machine learning and deep learning; Evaluate and benchmark AI models including LLMs, perform inference testing, optimise models and monitor model performance in production; Collaborate with fellow technical leaders to ensure the successful and timely delivery of models and integration of services; Contribute to the architecture of generative AI, including data, model, training, and evaluation, employing best practices; Develop production code and advocate for the best coding and engineering practices
Requirements: Demonstrated experience in designing and implementing scalable AI models for production; Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers; Practical experience with the latest technologies in LLM and generative AI, such as parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering techniques like Tree-of-Thoughts

Duties: Understand customer challenges and suggest them actionable insights with clear business benefit of the proposed solution; Work with large, complex data sets and applying advanced analytical methods as needed; Develop deployable machine learning solutions that address the real root cause; Interact with multiple stakeholders (from different levels) and effectively present findings by exploiting visual displays of complex quantity information in a simplified way; Be the partner of choice regarding technology advisory for advanced analytics
Requirements: Knowledge in advanced data analytics/data science; Have five years of relevant experience with data science/optimization/statistical models; Bachelor’s degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Compute Science, Mathematics, Physics, Electrical Engineering, Industrial Engineering) or related field

Duties: Use machine learning and analytical techniques to create scalable solutions for business problems; Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes; Design, development, evaluate and deploy innovative and highly scalable models for predictive learning; Research and implement novel machine learning and statistical approaches; Work closely with software engineering teams to drive real-time model implementations and new feature creations; Work closely with business owners and operations staff to optimize various business operations
Requirements: 3+ years of building models for business application experience; PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience; Experience in patents or publications at top-tier peer-reviewed conferences or journals; Experience programming in Java, C++, Python or related language; Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Duties: Responsible for the timely delivery of robust and visible demand forecasts for strategic brands/therapy areas and key markets, including clear documentation of assumptions as well as levels of confidence/robustness; Authoritatively/confidently present and defend forecast approach, methodology, assumptions and output to senior leadership; Ensure objectivity and robustness around the forecast via identification of the right data sources and application of industry-leading modeling approaches, with sensitivity analyses around key variables to assist in opportunity and risk management; Demonstrate a high level of understanding of the points of integration of our work and that of our colleagues; Develop new product introduction volume forecasts, both short-term and long-term, to foster into strategic and operational planning processes
Requirements: Bachelor’s Degree in relevant field; 6+ years of direct Pharmaceutical forecasting experience with an in-depth knowledge of forecasting techniques, models and approaches; Good understanding of one or more disease areas and how patients move through their respective diagnosis and treatment pathways, including treatment dynamics; Experience integrating insights from market research and secondary data analysis into forecast assumptions

Duties: Take a leadership role and drive statistical thinking in cross-functional team projects, within an international environment; Authoring statistical sections of important clinical document, SAP, CRS, Protocols etc and reviewing other sections from a statistical perspective; Contribute to Analysis and Reporting activities of internal delivered studies, including interim analyses; Lead the delivery and oversight on drug project(s) and/or studies to time, quality, and standards, including partnering with CROs and with Statistical Programming; Contribute to development of new procedures and standards to increase quality, efficiency, and effectiveness
Requirements: MSc/PhD in Statistics or Mathematics (containing a substantial statistical component) with experience in pharma/healthcare or other relevant area; Knowledge of the technical and regulatory requirements related to the role; Leadership capabilities to be able to lead and direct study and project work; Experience of data analysis and interpretation in a relevant area; Proficient in use of SAS including experience with development of reusable code

Duties: Build and maintain scalable machine learning solutions in production; Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness; Demonstrate end-to-end understanding of applications and develop a deep understanding of the “why” behind our models & systems; Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements and define the scope of the systems needed; Work closely with data platform teams to build robust scalable batch and realtime data pipelines; Work closely with software engineers, build tools to enhance productivity and to ship and maintain ML models
Requirements: 5+ years of applied ML experience; Proficiency in Python is preferred. We will also consider strong quantitative candidates with a background in other programming languages; Strong background in the foundations of machine learning and building blocks of modern deep learning; Track record of building, shipping and maintaining machine learning models in production in an ambiguous and fast paced environment; You have a clear understanding of frameworks like - PyTorch, TensorFlow, or Keras, why and how these frameworks do what they do; Familiarity with ML Ops concepts related to testing and maintaining models in production such as testing, retraining, and monitoring

Duties: As a Principal Engineer on the ITSM team, you will get the opportunity to work on cutting-edge AI and ML algorithms that help modernize IT Operations by reducing MTTR (mean time to resolve), and MTTI ( Mean time to identify). You will use your software development expertise to solve difficult problems, tackling complex infrastructure and architecture challenges
Requirements: 10+ years of total experience; Fluency in Python; Solid understanding of machine learning concepts and algorithms, including supervised and unsupervised learning, deep learning, and NLP; Familiarity with popular ML libraries like sci-kit-learn, Keras/TensorFlow/PyTorch, numpy, pandas; Good Understanding of Machine Learning project lifecycle

Duties: Design and develop machine learning models to drive impactful decisions; Utilize cutting-edge machine learning approaches and construct sophisticated machine learning models, including deep learning architecture on big data platforms; Work closely with the senior management team to develop ambitious, innovative modeling solutions and deliver them into production; Collaborate with various partners in risk, technology, model governance, etc. throughout the entire modeling lifecycle (development, review, deployment, and use of the models)
Requirements: Ph.D. or Master’s degree from an accredited university in a quantitative field such as Computer Science, Mathematics, Statistics, Econometrics or Engineering; Hands-on experience in quantitative data management and analytics, including data extraction, cleaning, transformation and visualization; Extensive practical experience and work experience with Machine Learning (supervised and unsupervised). Deep Learning (neutral network) experience is preferred; Experience working with engineering teams to operationalize Machine Learning models