5 Ml Lifecycle Jobs

As an Associate Manager of Machine Learning Services, your primary responsibility will be to collaborate effectively with Product, Services, Engineering, and Data Science teams to develop and deliver AI products. You will be required to engage with both internal and external stakeholders to comprehend use cases and provide guidance on suitable solutions. Additionally, you will need to adeptly translate intricate technical concepts into understandable terms for non-technical stakeholders. Your daily tasks will encompass a variety of activities such as data annotation, AI quality monitoring, and KPIs monitoring. You will also play a crucial role in training the team on AI-specific configurations, AI model development, delivery processes, and data analysis. Proficiency in office tools like Lucid Charts, Confluence, JIRA, and the MS Office suite will be essential for creating well-structured professional documentation. In this role, you will be expected to discern responsibilities and priorities, anticipate potential challenges, and effectively communicate them to the team. You may also be required to handle escalations, address high-priority incidents, delegate tasks within the team, and foster collaborative relationships among stakeholders to achieve resolutions. Furthermore, you will be responsible for establishing systems and procedures to ensure the quality of service delivery, developing collaborative delivery plans, and being accountable for service delivery performance while meeting customer expectations and contributing to future demand. People management tasks, including staff recruitment, performance evaluation, and training, will also be part of your responsibilities. The ideal candidate for this role should hold a full-time bachelor's or master's degree in Management, Engineering, or Communication, and possess 3-5 years of experience in ML project management, AI quality monitoring, or a similar role. Familiarity with Agile and Scrum processes, as well as previous experience in service delivery, data management, team management, client communication, and documentation, will be advantageous. Strong communication skills, both written and verbal, will be crucial for creating training materials, reviewing project documents, and ensuring clear communication with internal teams and clients. Additionally, organizational and interpersonal skills will be necessary to maintain project clarity, facilitate timely interactions, and ensure thorough testing of deliverables. Collaboration skills will also be essential for working with the Manager to establish team goals, plan strategies, and propose innovative solutions for team growth. Taking ownership of critical incidents, coordinating resolutions, and fostering effective post-incident communication among stakeholders will be part of your role. You will also be required to analyze processes, develop optimization strategies, and ensure that systems are in place to support exceptional service delivery. Through a deep understanding of projects, you will gain insights into the scope of service delivery and drive improvements to meet customer expectations and future demands.,

Service Cloud by Salesforce is a revolutionary customer service platform designed to transform your business's approach to customer support. It empowers your team to deliver efficient, personalized customer service experiences across multiple channels, all in one unified platform. With Service Cloud, you can streamline workflows, automate routine tasks, and leverage AI-powered insights to drive customer satisfaction and loyalty. Whether it's faster case resolution, 360-degree customer views, or real-time analytics, Service Cloud is your key to unlocking exceptional customer service. Join our dynamic team at Salesforce and make a real impact with your work! We're looking for passionate individuals who want to shape the future of our products and deliver exceptional data-driven experiences to our customers. Come be a part of our innovative and collaborative environment, where your skills will thrive, and your ideas will be valued. Apply now, and let's build something extraordinary together! We embrace the hybrid model and celebrate the individual strengths of each team member while encouraging everyone on the team to grow into the best version of themselves. We believe that autonomous teams with the freedom to make decisions will empower individuals, products, companies, and customers to thrive. Your Impact: Our ideal candidate is passionate about crafting a new product to serve thousands of enterprise customers by using innovative technology in AI and striving for the best quality and throughput. As part of this role, you will: - Design, implement, test, and deliver highly scalable enterprise-grade AI/ML solutions from the ground up. - Lead the development of prototypes and rapid experimentation to evaluate new algorithms, models, or approaches, balancing time-to-market with long-term technical investments. - Operate optimally in the hybrid engineering model where engineers are encouraged to craft and complete the vital work to ensure quality in their code as well as in other engineers". - Present your own designs to internal/external groups and review the designs of others. - Develop test strategies, design automation frameworks, write unit/functional tests to drive up code coverage and automation metrics for AI/ML systems. - Participate in the team's on-call rotation to address sophisticated problems in real-time and keep services operational and highly available. Required Skills: - 2 to 12 years of professional software development experience in implementing and operating AI/ML at scale. - Proven understanding in at least one high-level programming language (C++, Python, Go, etc.). - Deep domain knowledge and expertise in AI/ML and data analysis is required. - Experience in the ML lifecycle, including model training, deployment, inferencing, and the challenges associated with scaling AI products. - Proficiency in safe AI/ML rollout methodologies, including canarying and A/B testing. - Proven ability to design and architect complex, high-performance systems that support AI workloads. - Excellent interpersonal skills. - Degree or equivalent relevant experience required. Experience will be evaluated based on core competencies (e.g., extracurricular leadership roles, military experience, volunteer roles, work experience). Preferred Skills: - Strong technical expertise in Generative AI, particularly with RAG systems and Agentic workflows that use large language models. - Experience with Agile software development and test-driven development. - Track record of being a top performer in current and past roles. Benefits & Perks: Check out our benefits site which explains our various benefits, including wellbeing reimbursement, generous parental leave, adoption assistance, fertility benefits, and more!,

Job Summary: UPS Enterprise Data Analytics team is looking for a talented and motivated Data Scientist to use statistical modelling, state of the art AI tools and techniques to solve complex and large-scale business problems for UPS operations. This role would also support debugging and enhancing existing AI applications in close collaboration with the Machine Learning Operations team. This position will work with multiple stakeholders across different levels of the organization to understand the business problem, develop and help implement robust and scalable solutions. You will be in a high visibility position with the opportunity to interact with the senior leadership to bring forth innovation within the operational space for UPS. Success in this role requires excellent communication to be able to present your cutting-edge solutions to both technical and business leaderships. Responsibilities: Become a subject matter expert on UPS business processes and data to help define and solve business needs using data, advanced statistical methods and AI Be actively involved in understanding and converting business use cases to technical requirements for modelling. Query, analyze and extract insights from large-scale structured and unstructured data from different data sources utilizing different platforms, methods and tools like BigQuery, Google Cloud Storage, etc. Understand and apply appropriate methods for cleaning and transforming data, engineering relevant features to be used for modelling. Actively drive modelling of business problem into ML/AI models, work closely with the stakeholders for model evaluation and acceptance. Work closely with the MLOps team to productionize new models, support enhancements and resolving any issues within existing production AI applications. Prepare extensive technical documentation, dashboards and presentations for technical and business stakeholders including leadership teams. Qualifications Expertise in Python, SQL. Experienced in using data science-based packages like scikit-learn, numpy, pandas, tensorflow, keras, statsmodels, etc. Strong understanding of statistical concepts and methods (like hypothesis testing, descriptive stats, etc.), machine learning techniques for regression, classification, clustering problems, including neural networks and deep learning. Proficient in using GCP tools like Vertex AI, BigQuery, GCS, etc. for model development and other activities in the ML lifecycle. Strong ownership and collaborative qualities in the relevant domain. Takes initiative to identify and drive opportunities for improvement and process streamline. Solid oral and written communication skills, especially around analytical concepts and methods. Ability to communicate data through a story framework to convey data-driven results to technical and non-technical audience. Masters Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience. Bonus Qualifications NLP, Gen AI, LLM knowledge/experience Knowledge of Operations Research methodologies and experience with packages like CPLEX, PULP, etc. Knowledge and experience in MLOps principles and tools in GCP. Experience working in an Agile environment, understanding of Lean Agile principles.

Role Summary As a member of the Data Science Center of Expertise (DSCOE), the DS Lead Analyst is responsible for leading and enabling Data Science within Cigna Group with demonstrable aptitude in Data Science (i) Technical Skills (ii) Leadership (iii) Scope & Impact (iv) Influence. Please see Qualifications section below for more details. The role will support the development and maintenance of machine learning models, with a focus on ensuring that models meet Cigna s requirements for governance and legal compliance. The role will require collaboration with other data scientists and involve work across many lines of business. Key Responsibilities: Analyze model performance of new models with specific regards to requirements for legal compliance and governance standards around accuracy and bias; Perform periodic analyses of performance of existing models to ensure continued compliance with internal and external standards for accuracy and bias; Conduct research (i.e. literature review) to understand when bias may be biologically or medically justifiable, and to what degree, for example: finding evidence from literature that heart disease is more prevalent among older populations Using machine learning development tools to mitigate model bias when this is determined to be necessary Collaborating with data scientists, business stakeholders, and governance/compliance teams to ensure models meet compliance and governance standards Qualifications: Bachelors or Masters/PhD (preferred) in statistics or computer science or equivalent field with 5-8 years of relevant experience Strong proficiency in ML, statistics, python or R, SQL, version control (e.g., Git), health care data (e.g., claims, EHR) Ability to promote best coding practices, championing a culture of documentation/logging Thorough understanding of ML lifecycle, including necessary tradeoffs and associated risks Leadership in Data Science Can own a project end-to-end e.g., scoping, business value estimation, ideation, dev, prod, timeline Collaborates and guides junior team members in completion of projects and career development Works cross functionally with technical (e.g., Data Science, Data Engineering) and business (e.g., clinical, marketing, pricing, business analysts) to implement solutions with measurable value Scope and Impact Independently delivers clear and well-developed presentations for both technical and business audiences Creates data science specific project goals associated with project deliverables Articulates timeline changes, rationale, and goals to meet deadlines moving forward Values diversity, growth mindset, and improving health outcomes of our customers Level of Influence Communicate with stakeholders to identify opportunities and possible solutions based on business need Draft project charter, timeline, and features/stories Influence matrix-partner leadership

Role Summary: The AI/ML Platform Engineering Lead is a pivotal leadership role responsible for managing the day-to-day operations and development of the AI/ML platform team. In this role, you will guide the team in designing, building, and maintaining scalable platforms, while collaborating with other engineering and data science teams to ensure successful model deployment and lifecycle management. You can apply directly using the link below: https://jobs.lever.co/welocalize/628f87a7-4edd-4b4f-b440-3b602aa4dadc Key Responsibilities: Lead and manage a team of platform engineers in developing and maintaining robust AI/ML platforms. Define and implement best practices for machine learning infrastructure, ensuring scalability, performance, and security. Collaborate closely with data scientists and DevOps teams to optimize the ML lifecycle from model training to deployment. Establish and enforce standards for platform automation, monitoring, and operational efficiency. Serve as the primary liaison between engineering teams, product teams, and leadership. Mentor and develop junior engineers, providing technical guidance and performance feedback. Stay abreast of the latest advancements in AI/ML infrastructure and integrate new technologies where applicable. Qualifications: Bachelors or Masters degree in Computer Science, Engineering, or a related field. 8+ years of experience in AI/ML platform development and infrastructure. Proven experience in leading engineering teams and driving large-scale projects. Extensive expertise in cloud infrastructure (AWS, GCP, Azure), MLOps tools (e.g., Kubeflow, MLflow), and infrastructure as code (Terraform) Strong programming skills in Python and Node.js , with a proven track record of building scalable and maintainable systems that support AI/ML workflows. Hands-on experience with monitoring and observability tools, such as Datadog, to ensure platform reliability and performance. Strong leadership and communication skills with the ability to influence cross-functional teams. Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.