Full-Time

Engagement Lead

Risk Consulting/ CONSULT Manager I

Deadline 6/9/26
Marsh & McLennan

Marsh & McLennan

Compensation Overview

$130k - $197k/yr

+ Performance-based incentives

Company Does Not Provide H1B Sponsorship

New York, NY, USA

Hybrid

Must be within commutable distance to NY office; hybrid schedule requires at least 3 days on-site per week.

Category
Consulting (2)
,
Required Skills
Power BI
Python
SAS
Tensorflow
R
Neural Networks
Keras
Apache Spark
SQL
Machine Learning
Data Engineering
Tableau
AWS
Data Analysis
Requirements
  • Must have a Bachelors degree or foreign equivalent in Business Intelligence & Analytics, Computer Engineering, or a related field plus four (4) years of experience in the position offered or a related position.
  • Must have four (4) years of experience with all of the following: Utilizing intelligence and analytics in property and casualty insurance to support claims processes related to workers’ compensation, general liability, and automobile liability; Designing and creating business analytics and reporting tools to improve processes and support informed client decision-making; Developing a comprehensive understanding of data strategy frameworks, with a focus on their application to enterprise risk management, insurance analytics, and strategic decision-making processes; Conducting sophisticated, high-impact data analyses with a high degree of accuracy and consistency, leading complex projects with minimal oversight; Utilizing data manipulation libraries and visualization tools, including Power BI, Tableau, and RShiny, to analyze and present data effectively; Implementing machine learning algorithms and frameworks using TensorFlow, Keras, and RShiny to develop predictive models and enable data-driven decision-making; Cleaning, transforming, and preparing data from multiple sources to ensure suitability for analysis; Leveraging big data tools and frameworks, including AWS and Spark, for processing and analyzing large datasets; Possessing deep technical expertise within specialized domains, including advanced statistical modeling, machine learning, and data engineering techniques relevant to risk and insurance; Collaborating in cross-functional teams, working with data professionals, engineers, and business stakeholders; Programming in languages including Python and R for modeling and analysis; and Programming in SQL for database management and data manipulation.
Responsibilities
  • Work on enterprise-wide risk analytics, data strategy, and digital transformation initiatives.
  • Design and execute complex, multi-dimensional data analyses to generate transformative insights, support strategic decision-making, and inform industry benchmarks.
  • Lead cross-functional projects with limited oversight, supporting innovation and advancing data maturity across the organization.
  • Develop scalable and sustainable solutions that leverage cutting-edge technologies such as deep learning, artificial intelligence, automation, and real-time analytics.
  • Drive measurable business impact through improved risk management, underwriting, claims optimization, and enterprise risk quantification.
  • Carry out expertise in advanced statistical modeling, machine learning, artificial intelligence (AI), and data engineering, with a proven track record of deploying these techniques to address challenges in the risk and insurance domain.
  • Offer data-driven insights that support enterprise risk strategy, product development, and operational improvement.
  • Contribute to the development and implementation of comprehensive data strategy frameworks integrating them into holistic risk management architectures, insurance analytics ecosystems, and strategic planning efforts.
  • Serve as a trusted advisor to C-suite executives and board members, translating complex data insights into actionable strategies that support organizational growth and resilience.
  • Support high-level discovery initiatives, facilitate strategic workshops, and conduct executive interviews to identify latent data opportunities, define future-state data architectures, and build roadmaps for digital transformation and ongoing data maturity.
  • Maintain strong technical proficiency in tools such as Python, R, SAS, Tableau, Power BI, and cloud-based analytics platforms.
  • Build scalable, automated analytics pipelines to support enterprise-level decision-making.
  • Provide strategic guidance on RMIS architectures, vendor solutions, and integration strategies.
  • Lead enterprise-wide RMIS modernization initiatives, manage vendor negotiations and implementation processes, and ensure alignment with organizational risk appetite and compliance requirements.
  • Contribute to industry thought leadership through research publications, conference presentations, and mentorship, promoting innovation and continuous learning within the team.
  • Build toward future growth of risk management through data-informed insights and analytics-driven transformation.
Desired Qualifications
  • None

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