Manager Data Science Ngo Jobs in Riverside, CA (NOW HIRING)

Sr. Engineer Data Science & Agentic AI

As a Data Science & Agentic AI Sr. Engineer, you will develop, design, implement, and deliver advanced data science, machine learning, and agentic AI products for machine maintenance. You will create predictive and prescriptive maintenance solutions, including AI agents and multi‑agent workflows that retrieve knowledge, reason over data, use approved tools and APIs, and recommend or automate maintenance actions with appropriate human oversight.

• Perform data wrangling, exploratory, descriptive, predictive, prescriptive, and agent‑enabled analysis and visualization on both recurring and ad hoc basis to support the Projects Manager and the Maintenance user base.
• Identify new opportunities for intelligent process automation, Agentic AI, KPIs, visualizations, reports, dashboards, and decision‑support products aligned with leadership’s strategic objectives.
• Lead the entire data science, machine learning, and agentic AI lifecycle: problem definition, data collection, model/agent design, deployment, monitoring, governance, and continuous improvement.
• Ensure seamless integration and coordination across data pipelines, ML/DL models, LLM applications, agentic workflows, APIs, and business processes, optimizing safety, scalability, efficiency, and business impact.
• Establish robust monitoring mechanisms for deployed models and AI agents, enabling proactive identification of performance, reliability, drift, safety, cost, and governance issues.
• Define and execute overall machine learning, deep learning, and Agentic AI strategy aligned with business goals, maintenance reliability priorities, and enterprise technology standards.
• Design, build, and deploy AI agents and multi‑agent workflows using LLMs, RAG, vector search, tool/function calling, workflow orchestration, and secure API integrations.
• Integrate agentic workflows with CMMS/EAM, maintenance, asset, IoT, historian, PLC/SCADA, cloud, and enterprise data platforms while maintaining human‑in‑the‑loop controls for higher‑risk actions.
• Establish AgentOps, LLMOps, and MLOps practices for prompt/version management, agent evaluation, observability, guardrails, traceability, cost monitoring, model drift detection, and continuous improvement.
• Implement agentic AI safety, privacy, and security controls, including least‑privilege access, data protection, prompt‑injection mitigation, approval gates, audit trails, and responsible AI governance.
• Drive large‑scale data science, ML/DL, and Agentic AI projects leveraging data transformation, machine learning models, LLM applications, and intelligent workflow automation.
• Develop predictive maintenance tools, AI agents, and insights for customers while balancing data complexity, coding/visualization platforms, reliability requirements, risk controls, and client demands.
• Automate and streamline projects, reports, maintenance workflows, and agent‑enabled decision processes to increase efficiency, scalability, and adoption.
• Document project requirements, methodologies, architecture decisions, agent workflows, evaluation results, risks, and outcomes.
• Prepare technical reports, presentations, and user guides to communicate AI/ML/Agentic AI solutions to stakeholders.
• Stay updated with the latest advancements in AI/ML, Agentic AI, LLMs, RAG, vector search, orchestration frameworks, and industrial automation; conduct research and experiments to improve models and agents.
• Ensure compliance with ethical standards, legal requirements, privacy, bias, explainability, autonomy, human oversight, and potential misuse of AI/ML models or AI agents.
• Share expertise in AI/ML, Agentic AI, responsible automation, and insights with colleagues, stakeholders, and team members; conduct training sessions or workshops.
• Actively collaborate with project management teams, IT professionals, reliability engineers, maintenance leaders, and business stakeholders to identify opportunities for AI agents, ML models, and automation.

• 5–7 years experience in Python, R, or another programming language.
• 5–7 years experience with TensorFlow, PyTorch, scikit‑learn, or comparable ML frameworks.
• 3–5 years experience in Industrial ML, Automation, Data Science, AI, or related fields.
• 3–5 years experience with cloud computing platforms such as AWS, Azure, or GCP.
• 2–4 years experience with natural language processing (NLP), LLM applications, prompt engineering, or RAG.
• 2–4 years experience designing or deploying Agentic AI solutions, AI agents, RAG applications, LLM‑powered workflows, or intelligent automation.
• 2–4 years experience with agent orchestration and LLM application frameworks or platforms such as LangChain, LlamaIndex, Microsoft Semantic Kernel, AutoGen, Azure AI Foundry, OpenAI API, or equivalent.
• 3–5 years experience with Deep Learning, Computer Vision, Reinforcement Learning, or advanced predictive modeling.
• 2–4 years experience with ethical, legal, privacy, security, and responsible AI considerations in machine learning and Agentic AI systems.
• 2–4 years experience implementing AI guardrails, prompt/agent evaluation, telemetry, human‑in‑the‑loop review, and model or agent monitoring.

• 7–10 years experience in Python, R, or another programming language.
• 7–10 years experience with TensorFlow, PyTorch, scikit‑learn, or comparable ML frameworks.
• 5–7 years experience in Industrial ML, Automation, Data Science, AI, or related fields.
• 5–7 years experience with cloud computing platforms such as AWS, Azure, or GCP.
• 3–5 years experience with NLP, LLM applications, prompt engineering, or RAG.
• 3–5 years experience leading production Agentic AI, LLM, RAG, or multi‑agent orchestration initiatives in industrial or enterprise operations environments.
• 5–7 years experience with Deep Learning, Computer Vision, Reinforcement Learning, or advanced predictive modeling.
• 3–5 years experience with ethical, legal, privacy, security, and responsible AI considerations in machine learning and Agentic AI systems.
• 3–5 years experience with AgentOps/LLMOps practices, including monitoring, evaluation, versioning, safety testing, audit trails, and cost/performance optimization.

• Proficiency in Azure ML Studio and related tools for model development, deployment, and monitoring.
• Proficiency in Agentic AI and LLM application development, including prompt engineering, RAG, vector search/embeddings, function/tool calling, and agent workflow orchestration.
• Experience with Agentic AI frameworks or platforms such as LangChain, LlamaIndex, Microsoft Semantic Kernel, AutoGen, CrewAI, Azure AI Foundry, OpenAI API, or equivalent.
• Ability to design secure AI agent integrations with APIs, databases, CMMS/EAM platforms, cloud services, and industrial data sources while enforcing least‑privilege access and approval gates.
• Ability to evaluate and monitor AI agent performance using offline and online evaluations, trace logs, quality metrics, guardrails, human feedback, and incident response processes.
• Understanding of Responsible AI, privacy, prompt‑injection risks, model/tool misuse, auditability, and governance for autonomous or semi‑autonomous AI agents.
• Proficiency in query languages such as SQL, Hive, Pig.
• Proficiency in Microsoft Office Applications – Word, Excel, PowerPoint, Outlook, Project, Visio.
• Proficiency in applied statistical skills, such as distributions, statistical testing, regression.
• Basic understanding of data acquisition and processing tools and techniques and developing algorithms on common platforms to generate outputs.
• Scripting and programming skills such as Python, SQL, JavaScript, C++, C#, or API‑based integration for analytics, automation, and AI agent tool development.
• Basic understanding of PLC/SCADA systems such as SIEMENS S7, ALLEN BRADLEY, BnR, Edge data Management.
• Understanding of machine learning techniques and algorithms such as k‑NN, Naive Bayes, SVM, decision forests, gradient boosting, neural networks, and LLM‑based approaches.
• Preferred experience with common data science and AI toolkits such as R, Weka, Python, NumPy, Matplotlib, Pandas, MATLAB, Azure ML, and LLM/agent development libraries.
• Ability to translate data, model outputs, and AI agent recommendations into actionable decisions for senior management.
• Strong analytical and problem‑solving skills.
• Self‑motivated with a proven record of taking initiative.
• Detail‑oriented with excellent oral and written communication skills.
• Ability to execute tasks in a very dynamic and ever‑changing environment.

• Minimum Required: Bachelor’s Degree in Computer Science, Data Science, Artificial Intelligence, Industrial/Automation Engineering, or related fields.
• Preferred: Master’s Degree in Computer Science, Data Science, Artificial Intelligence, Industrial/Automation Engineering, or related field.

• Required: N/A
• Preferred: N/A

Salary: $111,766.36 – $159,267.07 per year.

• Paid Time Off for holidays, sick time, and vacation time.
• Paid parental and caregiver leaves.
• Medical, including virtual care options.
• Dental, Vision.
• 401(k) with company match.
• Health Savings Account with company match.
• Flexible Spending Accounts.
• Expanded mental wellbeing benefits including free counseling sessions for all team members and household family members.
• Family Building Benefits including enhanced fertility benefits for IVF and fertility preservation plus adoption, surrogacy, and Doula reimbursements.
• Income protection including Life and AD&D, short and long‑term disability, critical illness and an accident plan.
• Special discount programs including pet plans, pre‑paid legal services, identity theft, car rental, airport parking, etc.
• Tuition reimbursement, college savings plan and scholarship opportunities.

*Los Angeles County applicants only*

Niagara Bottling LLC is an Equal Opportunity Employer that does not discriminate on the basis of race, color, religion, sex, age, sexual orientation, gender identity and/or expression, national origin, veteran status or disability in relation to our recruiting, hiring, and promoting practices.