logoAiPathly

Analytics Solutions Associate

first image

Overview

The role of an Analytics Solutions Associate at JPMorgan Chase encompasses a diverse set of responsibilities and requirements, varying slightly based on the specific team or department. This overview provides a comprehensive look at the key aspects of the position:

Job Responsibilities

  • Data Management and Analysis:
    • Manage projects related to client profitability data and reporting improvements
    • Conduct root cause analyses on issues impacting client profitability
    • Develop and implement data governance processes
    • Manage data onboarding pipelines
  • Stakeholder Collaboration:
    • Work closely with business and technology stakeholders
    • Partner with senior leaders to translate business requirements into actionable results
  • Project Management:
    • Lead initiatives and maintain project plans
    • Manage milestones, risks, and status reports
  • Communication:
    • Prepare data-driven communications and presentations
    • Develop analytics to inform business health

Required Qualifications

  • Education: Bachelor's degree in Finance, Accounting, Data Science, or related field
  • Experience: 4+ years in financial services, business analytics, or project management
  • Technical Skills: Proficiency in Excel, SQL, Alteryx, and business intelligence tools
  • Analytical Skills: Advanced qualitative and quantitative analysis capabilities
  • Communication: Strong written and verbal communication skills
  • Leadership: Proven project management and team leadership abilities

Preferred Qualifications

  • Advanced technical skills (e.g., Python, AWS, Databricks)
  • Knowledge of data governance and protection
  • Understanding of business process reengineering
  • Prior experience in financial services

Work Environment

  • Typically a hybrid model combining remote and on-site work This role demands a blend of analytical prowess, technical expertise, and strong communication skills, coupled with the ability to manage multiple projects in a fast-paced environment. The ideal candidate will be adept at translating complex data into actionable insights and working collaboratively across various teams and levels of management.

Core Responsibilities

The Analytics Solutions Associate at JPMorgan Chase plays a crucial role in leveraging data to drive business decisions and improve processes. Key responsibilities include:

Data Analysis and Management

  • Coordinate and manage projects related to client profitability data and reporting improvements
  • Conduct in-depth analyses to identify root causes of issues impacting client profitability
  • Develop and implement data governance processes, ensuring data is properly classified, stored, and shared
  • Manage data onboarding pipelines and assist in defining data ownership roles

Stakeholder Collaboration

  • Build and maintain relationships with business stakeholders, project managers, and subject matter experts
  • Work closely with technology partners to deliver proactive solutions and prioritize requests
  • Partner with senior leaders to advance business analytics initiatives

Project Management

  • Lead initiatives and maintain comprehensive project plans
  • Manage milestones, risks, and status reports
  • Drive teams to deliver on initiatives while managing a complex analytics workload

Analytics and Reporting

  • Utilize advanced analytical skills to synthesize large datasets and identify key insights
  • Develop new Intelligent Solutions using tools like Alteryx and Tableau
  • Prepare and distribute data-driven communications and profitability presentations

Strategic Initiatives

  • Define, develop, monitor, and report key management metrics for global operations programs
  • Contribute to operational efficiency, cost modeling, and capacity planning efforts

Technical Expertise

  • Leverage a range of tools including SQL, Excel, Cognos, and SharePoint
  • Develop proficiency in additional technologies such as Qlik Sense, Python, and AWS as needed This role requires a unique blend of analytical skills, technical proficiency, and business acumen. The Analytics Solutions Associate must be able to translate complex data into actionable insights, effectively communicate with various stakeholders, and drive data-driven decision-making across the organization.

Requirements

To excel as an Analytics Solutions Associate at JPMorgan Chase, candidates should possess a combination of education, experience, and skills:

Education

  • Bachelor's degree in a quantitative discipline such as:
    • Finance
    • Accounting
    • Data Science
    • Management Information Systems
    • Business
    • Economics
    • Engineering
    • Statistics
    • Mathematics

Experience

  • Associate level: 4+ years in financial services, business analytics, or project management
  • Senior Associate level: 5+ years in data analysis, Business Intelligence, or Management Consulting

Technical Skills

  • Proficiency in:
    • SQL
    • Excel
    • Tableau or similar visualization tools
    • Alteryx
  • Familiarity with:
    • Python
    • Big data technologies (e.g., Teradata, Snowflake, DataBricks)
    • Cloud platforms (e.g., AWS)
  • Experience with SQL and NoSQL database systems

Analytical and Problem-Solving Skills

  • Strong quantitative and analytical capabilities
  • Ability to analyze large datasets and translate metrics into insights
  • Problem-solving skills with a focus on data-driven solutions

Communication and Project Management

  • Excellent written and verbal communication skills
  • Ability to present complex information to various levels of management
  • Strong project management capabilities
  • Experience with Agile methodologies and tools like Jira

Business Acumen

  • Understanding of financial services industry and business processes
  • Ability to translate business requirements into actionable results
  • Capacity to understand technical systems and their business implications

Interpersonal Skills

  • Ability to build and maintain relationships with diverse stakeholders
  • Strong collaboration and teamwork skills
  • Capacity to influence, negotiate, and manage conflicts

Additional Desirable Qualities

  • Adaptability to a fast-paced, dynamic environment
  • Self-motivation and ability to work independently
  • Attention to detail and commitment to high-quality deliverables The ideal candidate will bring a unique combination of technical expertise, business understanding, and interpersonal skills to drive data-informed decision-making and process improvements across the organization.

Career Development

The role of an Analytics Solutions Associate at JPMorgan Chase offers significant opportunities for professional growth and skill enhancement in data analytics, project management, and business strategy.

Responsibilities and Skill Development

  • Develop intelligent solutions using tools like Alteryx and Tableau
  • Coordinate client profitability data and reporting improvements
  • Conduct root cause analyses for key issues
  • Enhance analytical, critical thinking, and project management skills
  • Improve communication abilities with various stakeholders

Collaboration and Networking

  • Work closely with business and technology stakeholders
  • Build key relationships across different departments and management levels
  • Gain a broader understanding of organizational operations

Professional Growth Opportunities

  • Participate in programs like the Focused Analytics Solutions Team (FAST)
  • Develop creative and practical problem-solving skills in a consulting-style environment
  • Gain exposure to senior leaders and work on high-visibility, strategic projects

Technical Skill Enhancement

  • Improve proficiency in Tableau, Alteryx, SQL, Excel, and potentially Python and Qlik Sense
  • Gain experience with big data technologies and machine learning

Career Progression

  • Potential advancement to roles such as Senior Analyst, Manager, or transition into data science, risk management, or business strategy

Company Culture and Benefits

  • Emphasis on diversity, inclusion, and supportive work environment
  • Comprehensive health care, retirement savings plans, and professional development opportunities

Work Environment

  • Hybrid work model, balancing remote and on-site work for improved work-life balance The Analytics Solutions Associate role at JPMorgan Chase provides a solid foundation for career advancement in data analytics and business strategy, making it an attractive opportunity for those interested in these fields.

second image

Market Demand

The demand for Analytics Solutions Associates, particularly within prestigious financial institutions like JPMorgan Chase, is robust and driven by several factors:

Data-Driven Decision Making

  • Growing need for professionals who can analyze complex data sets
  • Emphasis on developing actionable insights to drive business decisions
  • Increased focus on data strategy, governance, and analytical solutions

Advanced Technology Adoption

  • Financial services sector increasingly adopting big data, machine learning, and cloud technologies
  • Demand for skilled professionals to manage and analyze large datasets
  • Need for expertise in tools like Tableau, Alteryx, and SQL

Cross-Functional Collaboration

  • Roles require effective communication with multiple stakeholders
  • Ability to translate complex technical information for various audiences
  • Project management skills across different functional areas

Strategic and Operational Support

  • Continuous need for analytics solutions in investment banking, consumer and small business banking, commercial banking, and asset management
  • Crucial role in leading daily operations and managing cross-functional programs
  • Importance of ensuring data governance and risk management

Innovation and Talent Development

  • Significant investment in technology by financial institutions
  • Commitment to innovation and talent development
  • Ample opportunities for career growth and learning The demand for Analytics Solutions Associates is driven by the financial sector's increasing reliance on data analytics, adoption of advanced technologies, and the need for skilled professionals to manage complex data sets and drive business decisions.

Salary Ranges (US Market, 2024)

Based on available data and market trends, here's an estimate of salary ranges for Analytics Solutions Associates in the US market as of 2024:

Average Salary

  • Approximately $70,000 to $85,000 per year

Salary Range

  • Typically between $60,000 and $90,000 annually
  • Varies based on experience, location, and other factors

Factors Influencing Salary

  • Education and certifications
  • Years of experience
  • Additional skills (e.g., programming languages, data visualization tools)
  • Geographic location (higher in major tech hubs and financial centers)
  • Company size and industry
  • Data Analyst: Average salary around $84,352, range from $44,000 to $170,000
  • Analytics Associate: Average salary about $71,635, range from $64,394 to $80,650

Career Progression

  • Potential for salary growth with experience and advancement to senior roles
  • Opportunities for bonuses and performance-based incentives in many financial institutions

Benefits and Perks

  • Many employers offer comprehensive benefits packages
  • May include health insurance, retirement plans, and professional development opportunities Note: Salary data is approximate and subject to change. Always research current market rates and consider the total compensation package when evaluating job opportunities.

Data analytics in the financial services sector is rapidly evolving, with several key trends shaping the role of Analytics Solutions Associates:

  1. Data Governance and Strategy: There's an increased focus on establishing robust data governance frameworks and strategies to manage data risk effectively.
  2. Advanced Technologies: The integration of big data, machine learning, and cloud infrastructure is driving business decisions and enhancing customer experiences.
  3. Cross-Functional Collaboration: Analytics professionals must work across various departments, requiring strong communication and project management skills.
  4. Consulting Approach: A problem-solving orientation is essential, involving the development of strategic roadmaps and effective communication of insights to stakeholders.
  5. Customer-Centric Solutions: Analytics solutions are designed to create competitive advantages and improve customer satisfaction through data-driven insights.
  6. Soft Skills Emphasis: Beyond technical expertise, skills such as communication, creativity, and adaptability are highly valued.
  7. Hybrid Work Environment: The industry is adopting flexible work arrangements, combining remote and on-site work.
  8. Innovation and Career Development: There's a strong commitment to fostering innovation and providing opportunities for professional growth and advancement. These trends highlight the dynamic nature of data analytics in finance, emphasizing the need for strategic thinking, technological proficiency, and strong collaborative skills.

Essential Soft Skills

Success as an Analytics Solutions Associate requires a blend of technical expertise and crucial soft skills:

  1. Communication: Ability to present findings clearly and concisely, including data storytelling and translating complex data into actionable insights for non-technical stakeholders.
  2. Problem-Solving: Strong analytical reasoning and innovative thinking to objectively analyze issues and make data-driven decisions.
  3. Interpersonal Skills: Building and maintaining relationships with team members and stakeholders across various departments.
  4. Project Management: Managing multiple projects, prioritizing tasks, and working independently in a fast-paced environment.
  5. Critical Thinking: Making sound judgments and connecting data points to derive meaningful insights for strategic decision-making.
  6. Time Management: Efficiently handling multiple projects simultaneously and keeping them on track.
  7. Client Engagement and Influence: Engaging with clients, influencing business partners, and negotiating across all levels of the organization.
  8. Adaptability: Demonstrating flexibility and creativity in problem-solving, thriving in dynamic environments. These soft skills complement technical abilities, enabling Analytics Solutions Associates to effectively communicate insights, collaborate with stakeholders, and drive business decisions through data-driven solutions.

Best Practices

To ensure the success of analytics solutions, consider the following best practices:

  1. Strategic Data Approach: Align business questions with appropriate data sources, balancing acquisition costs with potential value.
  2. Develop Analytics Products: Focus on scalable solutions that generate measurable financial benefits and improve business performance.
  3. Stakeholder Alignment: Engage stakeholders early to define goals, questions, and KPIs, ensuring analytics efforts align with business objectives.
  4. Build High-Performance Teams: Foster a culture that integrates analytics expertise across business functions, embracing hypothesis-based methodologies.
  5. Enhance Data Literacy: Use descriptive analytics to build understanding and appreciation for data-driven decision-making.
  6. Ensure Compliance and Governance: Adhere to regulations and establish formalized processes for data management.
  7. Continuous Model Refinement: Regularly update analytics models to reflect changing business conditions and new data.
  8. Effective Data Storytelling: Present insights using visuals, context, and narratives that highlight financial benefits and actionable outcomes.
  9. Practical Application: Gain hands-on experience with cloud-based analytics solutions, leveraging resources like AWS Skill Builder and hands-on labs.
  10. Continuous Learning: Stay updated with the latest trends and technologies in data analytics through ongoing education and skill development. By implementing these practices, organizations can create effective, scalable analytics solutions that drive informed decision-making and improve overall performance.

Common Challenges

Analytics Solutions Associates often face several challenges in their role. Here are some common issues and potential solutions:

  1. Data Quality Issues
  • Challenge: Incomplete, inaccurate, or inconsistent data
  • Solution: Implement comprehensive data validation, cleansing techniques, and robust data governance practices
  1. Data Integration
  • Challenge: Difficulty in merging data from various sources
  • Solution: Utilize ETL processes and data integration tools; consider developing a centralized data warehouse or data lake
  1. Resource Constraints
  • Challenge: Limited budgets, compute power, and staffing
  • Solution: Prioritize projects based on ROI; leverage scalable cloud-based solutions
  1. Data Security and Privacy
  • Challenge: Handling sensitive data while adhering to privacy regulations
  • Solution: Implement robust security measures, including encryption and access controls; ensure compliance with data privacy laws
  1. Lack of Domain Knowledge
  • Challenge: Gaps in understanding specific business areas
  • Solution: Collaborate with subject matter experts; conduct domain research and knowledge-sharing sessions
  1. Visualization Complexity
  • Challenge: Creating effective data visualizations, especially for large datasets
  • Solution: Leverage specialized visualization tools; improve data storytelling skills
  1. Scalability and Performance
  • Challenge: Analytics infrastructure struggling with growing data volumes
  • Solution: Adopt distributed computing frameworks or cloud-based solutions that auto-scale
  1. Change Management and Adoption
  • Challenge: Resistance to data-driven decision-making; low team involvement
  • Solution: Communicate the value of analytics; provide training and support; promote a data-driven culture
  1. Unclear Objectives
  • Challenge: Lack of clear goals leading to unfocused analytics efforts
  • Solution: Define specific, measurable objectives aligned with business needs before starting projects By addressing these challenges systematically, Analytics Solutions Associates can enhance the effectiveness of their data analytics operations and drive greater value for their organizations.

More Careers

Research Scientist AI

Research Scientist AI

An AI Research Scientist is a professional at the forefront of advancing artificial intelligence through rigorous research, development, and innovation. This comprehensive overview outlines their role, responsibilities, and key aspects of the profession: ### Key Responsibilities - Conduct cutting-edge research to advance AI state-of-the-art - Design and develop algorithms and models for complex AI problems - Design and conduct experiments to evaluate AI algorithms and models - Publish research findings and collaborate with the scientific community - Develop prototypes and proof-of-concept implementations ### Specializations AI Research Scientists can specialize in various areas, including: - Machine Learning - Computer Vision - Natural Language Processing - Deep Learning - Human-Robot Interaction - Reinforcement Learning ### Work Environment AI Research Scientists work in diverse settings, including: - Academic institutions - Research labs - Technology companies and startups Remote work opportunities have become increasingly common, offering flexibility and leveraging digital collaboration tools. ### Skills and Qualifications Successful AI Research Scientists typically possess: - Ph.D. or equivalent experience in Computer Science, AI, or related fields - Demonstrable research experience with publications in renowned journals - Proficiency in programming languages (e.g., Python, Java, R) and AI tools - Deep understanding of machine learning, neural networks, and computational statistics - Strong analytical, problem-solving, and communication skills ### Industry Role AI Research Scientists play a crucial role in: - Driving innovations in AI - Contributing to cutting-edge discoveries - Advancing technological capabilities - Collaborating with multidisciplinary teams - Influencing future AI applications across various sectors Their work shapes the future of AI technology and its impact on society.

Responsible AI Architect

Responsible AI Architect

Responsible AI is a comprehensive approach to developing, assessing, and deploying AI systems in a manner that is ethical, safe, trustworthy, and respectful of human values. As a Responsible AI Architect, you'll need to consider the following key principles and practices: ### Core Principles 1. **Fairness**: Ensure AI systems do not discriminate against different groups of stakeholders. Identify and mitigate biases in data and algorithms. 2. **Explainability**: Design transparent and explainable AI systems, providing human-understandable descriptions of predictions and behaviors. 3. **Privacy and Security**: Protect data and models by appropriately obtaining, using, and safeguarding them against breaches and malicious attacks. 4. **Safety**: Prevent harmful system outputs and misuse, designing systems that operate safely even in unanticipated conditions. 5. **Controllability**: Implement mechanisms to monitor and steer AI system behavior, ensuring management and correction capabilities. 6. **Veracity and Robustness**: Ensure correct outputs even with unexpected or adversarial inputs through robust testing and validation. 7. **Governance**: Incorporate best practices into the AI supply chain, including regulatory compliance, auditing, and clear accountability. 8. **Transparency**: Enable stakeholders to make informed choices by providing clear information about AI systems' workings, strengths, and limitations. ### Lifecycle Considerations 1. **Envisioning and Impact Assessment**: Conduct early-stage assessments to identify potential harms and benefits, addressing ethical, regulatory, and reputational risks. 2. **Data Collection and Processing**: Follow best practices for responsible data collection and processing to avoid bias and respect privacy. 3. **Prototyping and Testing**: Design and test AI system prototypes with responsible AI principles in mind. 4. **Deployment and Monitoring**: Deploy systems with human oversight mechanisms and ongoing monitoring, regularly auditing for ethical compliance. ### Tools and Practices 1. **Responsible AI Dashboards**: Use dashboards to quantify and communicate risks, including model interpretability and error analysis. 2. **Human Oversight**: Integrate mechanisms for human oversight in critical decision-making processes. 3. **Collaboration and Industry Standards**: Stay informed about the latest developments in responsible AI practices and adhere to industry standards and regulatory requirements. By adhering to these principles and practices, a Responsible AI Architect ensures that AI systems are developed and deployed in a way that is safe, trustworthy, and respectful of human values.

Responsible AI Engineer

Responsible AI Engineer

A Responsible AI Engineer plays a crucial role in developing, implementing, and maintaining artificial intelligence systems that are safe, trustworthy, and ethical. This role combines technical expertise with a strong focus on ethical considerations and risk management. Responsibilities: - Develop and deploy AI systems that perform tasks such as learning from data, making predictions, and decisions - Ensure AI systems adhere to principles of fairness, privacy, and security - Manage risks and ensure the safety of AI systems - Optimize AI algorithms for performance and efficiency - Design and implement data pipelines - Integrate AI systems with other software applications Skills and Qualifications: - Proficiency in programming languages (Python, Java, C++) - Expertise in machine learning techniques and deep learning concepts - Strong mathematical background (statistics, probability, linear algebra, calculus) - System design and cloud computing platform experience - Collaboration and communication skills Principles of Responsible AI: - Fairness: Ensure AI systems are free from bias and discrimination - Reliability and Safety: Build systems that operate consistently and safely - Privacy and Security: Protect user data and ensure system security - Inclusiveness: Develop systems that respect diverse user needs - Transparency: Create interpretable AI systems - Accountability: Establish clear lines of responsibility Tools and Frameworks: - Responsible AI dashboards for monitoring performance and fairness - Risk management frameworks (e.g., NIST AI Risk Management Framework) - Collaboration tools for teamwork and version control In summary, a Responsible AI Engineer balances technical prowess with ethical considerations to develop AI systems that benefit all stakeholders while minimizing potential risks.

Research Scientist Machine Learning

Research Scientist Machine Learning

A Machine Learning Research Scientist is a specialized professional in the field of artificial intelligence (AI) and machine learning, responsible for advancing the state-of-the-art through research and development. Key responsibilities include: - Conducting cutting-edge research in machine learning - Designing and developing new algorithms and methodologies - Experimenting and evaluating machine learning models - Applying research to solve real-world problems Educational and skill requirements: - Advanced degree (Master's or Ph.D.) in a related field - Strong foundation in mathematics, computer science, and statistics - Proficiency in programming languages (e.g., Python, SQL) - Excellent research and communication skills Career path and work environment: - Progression from research assistant to independent researcher or team lead - Work settings include academic institutions, research labs, and industry Distinctions from related roles: - Focus on research and development, unlike Machine Learning Engineers who deploy models - More specialized in advancing specific domains compared to Data Scientists Machine Learning Research Scientists play a crucial role in pushing the boundaries of AI technology, requiring a unique blend of technical expertise, research acumen, and innovative thinking.