JPMorgan HR Data & Analytics - Insights Product Delivery Sr Associate 

United States, New Jersey, Jersey City 

29018148

As a Sr. Associate in the HR Data & Analytics team, you will analyze our large-scale global human capital and workforce data to create insights, analytical solutions, and customized models that answer critical business questions. You will translate business questions into analyses tasks, collaborate with internal subject matter experts (SMEs), build customized analytics solutions, and communicate customized results with relevant parties. You will participate in the design and delivery solutions (e.g., analytics dashboards, proprietary models, visualization schemes, etc.) to meet customers’ needs.

Job responsibilities

• Conduct analyses on workforce data to answer business questions from multiple stakeholders and support HR in making evidence-based decisions
• Understand data life cycle across technology ecosystem, collaborate with cross-functional teams in business & technology, and leverage a suite of tools to build analytical solutions
• Capture and understand end-user requirements, translate into customized analytical solutions, communicate insights via reports, dashboards, visualization etc.
• Create and deploy workflows for repeatable, scalable, and automated solutions
• Build data analytics pipelines, including quality checks, exploratory analysis, and collaborate with technology teams in production deployment.
• Develop, use, and implement innovative analytical workflow and modeling approaches that capitalize on data assets, identify best fit data insight tools possibly including advanced analytics and data science models, such as LLMs, etc.
• Project manage the design, build, and delivery of new analytical solutions with a pragmatic approach in evaluating multiple solutions
• Attention to detail, rigor, and robustness in data analytics and results. Ability to articulate complex issues in easy to understand ways
• Adherence to various control functions and regulatory requirements while handling workforce data

Required qualifications capabilities and skills

• Bachelors with 3+ years’ experience in a related data discipline (e.g., Computer Science, Economics, Business, IO Psychology, Statistics, Business Analytics, or relevant fields), and/or 2+ years at a top management consulting firm with a Master’s degree (or equivalent in industry)
• Hands-on experience in at least two of the following:
  • Statistical software or coding languages (e.g., Python, R, SAS)
  • Data analytics and visualization tools (e.g., Tableau, PowerBI, Qlik)
  • Statistical or quantitative analysis (e.g., multiple regression, multivariate analysis, network analysis, AI-ML concepts and techniques)
  • Advanced excel skills (e.g., pivot tables, VLOOKUP, Analysis ToolPak, macros/VBA)
  • Data wrangling, workflows, and automation (e.g., SQL, Alteryx, Business Objects, etc.)
• Versatile in learning and picking up different software, tools, methodologies, or coding languages
• Demonstrated ability to create custom solutions that solve business problem
• Demonstrated experience in presenting reports, insights, and data analytics findings
• Relevant experience in data & analytics topics in consulting, client engagement, or project execution

Preferred qualifications, capabilities and skills

• Domain knowledge in Human Resources analytics or in the financial services, especially in employee relations, recruitment, workforce planning, talent & career development, and HR service
• Experience with Natural Language Processing (NLP) algorithms, tools, customer/employee survey analyses, segment analysis and pattern detection, etc.
• Willingness to learn new areas of focus - especially support functions, compliance, global security, etc., as relates to HR matters
• Comfortable with ambiguity and stakeholder management across multiple business functions
• Familiarity with project managements concept, such as agile practices
• Familiarity with cloud computing approaches, such as AWS, Azure, etc
• Familiarity or hands-on experience with data science, machine learning, and AI