Program Manager

• Lead end-to-end delivery of multiple AI initiatives from discovery and solution design through deployment and hypercare
• Own scope, schedule, budget, quality, risk, and realized business outcomes
• Establish integrated plans across data engineering, model development, application engineering, and platform/MLOps workstreams
• Establish AI program governance, KPIs/OKRs, release gates, and audit-ready documentation
• Oversee estimation, staffing, capacity planning, and cross-team dependencies
• Ensure compliance with SDLC/MLLC, data governance, privacy, security, and model risk standards
• Lead delivery of AI/ML and GenAI solutions, including RAG, copilots, and intelligent automation
• Guide use-case framing, data readiness, experimentation, evaluation, fine-tuning, deployment, observability, and continuous improvement
• Manage AI risks involving data quality, bias/fairness, privacy, security, hallucinations, performance, scalability, cost, and adoption
• Partner with platform teams to implement MLOps/LLMOps capabilities
• Engage executives on outcomes, success metrics, decision cadence, delivery health, risks, and value realization
• Manage escalations and drive root-cause resolution
• Select and scale Agile, Waterfall, or Hybrid delivery models
• Lead cross-functional collaboration across engineering, product, design, QA, cloud/platform, and InfoSec
• Manage vendors and partners, including model/API providers
• Own budgeting, forecasting, cost control, and resource optimization
• Run proactive RAID management and AI testing/evaluation before release
• Institutionalize delivery best practices, conduct lessons learned, and coach project/program managers and engineering leaders

Data Scientist

• Design, develop, and deploy machine learning models for business and financial use cases
• Build predictive, classification, clustering, recommendation, and forecasting solutions
• Perform exploratory data analysis (EDA) to uncover trends, anomalies, and business opportunities
• Develop statistical models for financial planning, forecasting, risk assessment, and performance optimization
• Translate business requirements into analytical solutions and measurable outcomes
• Process and analyze large-scale structured and semi-structured datasets using PySpark/Spark
• Develop feature engineering pipelines for machine learning applications
• Work with distributed computing frameworks for scalable model training and inference
• Implement end-to-end ML lifecycle management using MLflow
• Build and maintain reusable feature pipelines using Feature Store frameworks

Manager

• Develop AI models
• Train machine learning models
• Collaborate with other engineers
• Implement and optimize AI algorithms
• Develop and implement cognitive computing solutions using AI and machine learning technologies
• Focus on complex algorithms and natural language processing (NLP)

Lead Assistant Manager

• Ensure adherence to operational and process-related controls, service delivery requirements, client contractual terms, policies, rules, and procedures in the client area
• Coordinate and support compliance activities in the client area
• Conduct periodic testing of process controls and service delivery compliance reviews/audits
• Monitor and coordinate, as necessary, compliance activities of operations with enabling departments and compliance teams
• Act as an independent reviewer and evaluation body to ensure compliance issues and concerns are appropriately evaluated, investigated, and resolved
• Provide regular reports and additional reports as directed or requested
• Conduct and lead audits to identify fraud, operational, regulatory, and legal risks
• Evaluate control effectiveness to ensure compliance and risk mitigation
• Interact with AM/LAM, Managers/Senior Managers, AVPs/VPs, audit teams, Corporate Compliance, and ISG/BCM/Privacy teams

Assistant Manager

• Support administration of global equity compensation programs, including Stock Options, Restricted Stock Units (RSUs), and Employee Stock Purchase Plans (ESPP)
• Process equity grants, vesting events, ESPP enrollments, and purchases
• Maintain accurate employee equity records in stock plan systems
• Monitor transactions and reconcile data with HRIS and payroll systems
• Support employee inquiries regarding ESPP, equity awards, and transactions
• Assist with regulatory and tax compliance
• Coordinate equity data with payroll, finance, and HR business partners
• Assist with reconciliation and reporting in the Fidelity equity platform
• Maintain equity plan procedures and policy documentation
• Provide equity samplings for audit and SOX controls
• Prepare equity reports and analytics for internal stakeholders
• Identify process improvements and system enhancements
• Assist with equity system maintenance and data audits
• Support annual grant cycles and Employee Stock Purchase Plan operations
• Partner with Human Resources, Finance and Accounting, Payroll, Legal, and external equity plan administrators and brokers
• Respond to employee inquiries and assist with communications, webinars, training materials, and equity education sessions

Manager – Corporate Rewards

Data Analyst

• Run basic data profiling on datasets to check completeness, accuracy, and consistency
• Document findings and flag data quality issues to the team
• Help prepare data quality reports for stakeholder review
• Learn and use Microsoft Purview for day-to-day data governance activities
• Register data assets and maintain data catalog entries
• Follow established governance processes and document steps
• Work with business teams to understand day-to-day data usage
• Draft definitions for business terms and basic data rules based on profiling results
• Maintain the business glossary
• Support standardization of data formats, naming conventions, and values across systems
• Flag inconsistencies and coordinate data clean-up activities
• Develop and manage small data informatics projects
• Ensure adherence to data informatics standards
• Develop and deliver training sessions
• Provide feedback to junior team members

• Perform financial planning and financial forecasting
• Conduct variance analysis
• Provide commentary and reporting
• Develop scenario-based models to support and evaluate business stress testing
• Ensure appropriate business reporting functions are established so each Business Function can measure Key Financial Performance Indicators and report functional performance to the business head
• Complete all responsibilities in accordance with Company policies
• Provide high-quality and timely advice, information, analysis, reporting, and interpretation to support effective decision-making and performance management

Data Engineer

• Drive stability, availability, and continuous improvement of enterprise data platforms through ETL operations, infrastructure support, stakeholder request management, and process optimization.
• Monitor daily ETL workflows, troubleshoot production issues, perform root cause analysis (RCA), and implement preventive solutions.
• Review technical requirements and code changes, test and deploy enhancements, and coordinate with vendors and upstream teams to incorporate source system changes.
• Monitor database and server performance, capacity, and resource utilization, proactively addressing issues and bottlenecks.
• Maintain operational documentation and manage client communications, including status updates, stakeholder alignment, and risk escalation.

Project Manager

• Execute web audit checklist items across customer journeys using GTM Preview and the browser DevTools network tab
• Confirm the correct event fires with the right parameters for product view, add to cart, checkout, purchase, sign-in, and form submission
• Validate GA4 events in DebugView, including event names, required parameters, data types, and absence of PII
• Run BigQuery data quality queries to identify (not set) volumes, missing purchase values, duplicate events, and tracking-spec mismatches
• Support AppsFlyer event validation by cross-referencing event logs against the tracking specification and documenting discrepancies
• Log audit findings with surface, category, root cause, evidence, and recommended fix
• Maintain tracking documentation, including event tables, parameter definitions, and implementation code snippet templates
• Prepare status reporting and governance meeting summaries, flagging new anomalies

Product Analyst

• Act as the Data Subject Matter Expert to stakeholders across the end-to-end data supply chain.
• Collect, clean, and analyze data from multiple sources to uncover trends, patterns, and actionable insights.
• Translate analytical findings into clear, business-friendly insights supporting decision-making.
• Elicit, document, and manage business, functional, and non-functional requirements through interviews, workshops, process analysis, and user story creation.
• Conduct stakeholder analysis to understand needs, roles, dependencies, and expectations.
• Support prioritization by assessing business value, impacts, complexity, and risk.
• Ensure traceability of requirements from intake through development, testing, and deployment.
• Collaborate with data engineering analysts, architects, and engineers to ensure solutions reflect business context and use cases.
• Define testing acceptance criteria, support user validation, and provide structured feedback for continuous improvement.
• Monitor and enhance data quality through profiling, validation, remediation, and alignment with organizational priorities.

Digital Data Product Analyst

Consultant

• Participate in end-to-end delivery of AI-enabled consulting engagements, from proposal through final outputs
• Structure problem statements, define analytical approaches, and translate findings into actionable recommendations
• Analyze business processes and operational data to identify inefficiencies, root causes, and improvement opportunities
• Conduct stakeholder workshops to capture processes, pain points, controls, data touchpoints, and AI enablement areas
• Develop current- and future-state artifacts, including process maps, SOPs, journey maps, and RACI, aligned to AI-driven transformation
• Perform data analysis and work decomposition to quantify impact, benefits, and business case value
• Create executive-ready presentations, narratives, and deliverables for senior stakeholders
• Collaborate with cross-functional teams to validate insights, shape AI-driven solutions, and track emerging AI and industry trends
• Support adoption through clear documentation, training inputs, and feedback incorporation
• Gather requirements, manage stakeholders, perform structured problem solving, and translate data and process insights into actionable outcomes
• Define data needs, perform data preparation, and support AI-driven reporting
• Identify and evaluate use cases based on feasibility, impact, and data availability
• Define high-level solution approaches, workflows, decision frameworks, validation approaches, checkpoints, and human intervention points
• Review and validate solution outputs for alignment with business expectations

Transformation Solution Consultant Lead

General

• Lead the design and development of advanced application solutions
• Define best practices for application development and deployment
• Oversee application project execution and resource management
• Ensure application solutions align with business objectives
• Provide technical leadership on application initiatives
• Manage a team of application engineers
• Set technical direction and manage project timelines
• Collaborate with other departments to align application development with organizational objectives
• Ensure successful implementation of software applications

Application Development Lead

• Lead and manage the business intelligence and visualization team
• Oversee delivery of BI solutions supporting business strategy and decision-making
• Collaborate with senior leadership to align BI initiatives with organizational objectives
• Drive innovation in data visualization and BI methodologies
• Ensure the professional development of team members

Software Engineer

• Design, build, and deploy Agentforce agents, Topics, and Actions for marketing automation and customer engagement workflows
• Configure Prompt Builder and integrate LLM-assisted decisioning into customer journeys and dynamic messaging
• Connect Agentforce with Salesforce Data Cloud and external APIs to provide real-time context and safely ground AI actions
• Establish guardrails, testing protocols, and compliance standards for AI-generated interactions
• Design relational data schemas, Data Extensions, and customer profiles for real-time AI decisioning and segmentation
• Build REST/SOAP API integrations, microservices, and webhooks connecting SFMC with internal backends, CDPs, and enterprise systems
• Write intermediate-to-advanced SQL queries for data transformations in Automation Studio and Data Cloud
• Build dynamic, personalized logic using AMPscript, SSJS, HTML, CSS, and JavaScript
• Develop custom CloudPages, interactive forms, and API-triggered entry sources in Journey Builder
• Troubleshoot complex data flows, API failures, and agent execution paths using root-cause analysis

Senior Developer – Marketing Cloud, Agentforce Engineer

Machine Learning Engineer

• Design, build, and deploy machine learning models and GenAI solutions for real business problems
• Develop end-to-end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and retraining
• Build and productionize GenAI applications involving LLM integration, prompt engineering, RAG pipelines, embeddings, vector databases, and agentic workflows
• Fine-tune, evaluate, and optimize classical ML, deep learning, and LLM models for accuracy, latency, and cost
• Deploy and operate models in production on AWS or comparable cloud platforms
• Implement MLOps practices including experiment tracking, model versioning, CI/CD, automated testing, and monitoring
• Design data pipelines, ensure data quality, and build feature stores with data engineering
• Establish evaluation frameworks for traditional and LLM-based systems
• Embed fairness, explainability, privacy, and security into model development and deployment
• Collaborate with product managers, architects, and client stakeholders to translate business requirements into measurable ML solutions
• Write clean, tested, production-quality code and participate in design and code reviews
• Build proofs of concept and harden successful experiments into production systems
• Mentor junior engineers and data scientists
• Stay current with ML/GenAI developments and recommend valuable models, frameworks, and techniques