Solution Architect - Jersey City

Reposted 9 Days Ago
Be an Early Applicant
2 Locations
In-Office or Remote
70K-245K Annually
Expert/Leader
Agency • Information Technology
The Role
Lead and architect end-to-end AI/ML and large-scale data solutions for investment banking and equity markets. Design data pipelines, model development and deployment, data mapping/integration, and front-end visualization. Drive data governance, ensure scalability and accuracy, mentor teams, and align cross-functional stakeholders to business goals.
Summary Generated by Built In

Key Responsibilities

Lead data and AI/ML initiatives tailored to investment banking or equity markets, aligning with business goals. 

Architect, implement, and optimize end-to-end AI/ML solutions designed for large-scale datasets, ensuring scalability and efficiency. 

Leverage AI techniques for data mapping and transformation, enabling seamless integration, unification, and enrichment of complex and heterogeneous datasets across diverse systems and platforms. 

Drive cross-functional collaboration, bridging technical teams and business stakeholders. 

Oversee data pipeline design, model development, and deployment with a focus on scalability and accuracy. 

Drive compliance with policies/standards (e.g., data quality) and adoption of data governance capabilities. 

Provide mentorship to teams, fostering innovation and excellence in delivery. 

Influence and drive the adoption of front-end solutions to support data visualization and user experiences. 

Qualifications

12+ years of experience, including significant domain expertise in investment banking or equity markets. 

Advanced degree in Computer Science, Data Science, or a related field. 

Extensive hands-on experience with Python, big-data frameworks, and AI/ML platforms. 

Proven expertise in designing and managing large-scale data ecosystems and leveraging AI for data integration and mapping. 

Proficiency in front-end development for data visualization or user-centric tools. 

Familiarity with cloud platforms (AWS, Azure, or GCP) and modern data infrastructure. 

Exceptional leadership, communication, and strategic thinking skills to manage cross-functional teams and deliver impactful solutions. 

Strong ability to thrive in fast-paced and high-pressure financial environments. 


Compensation, Benefits and Duration

Minimum Compensation: USD 70,000
Maximum Compensation: USD 245,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post

Skills Required

  • 12+ years of experience with significant domain expertise in investment banking or equity markets
  • Advanced degree in Computer Science, Data Science, or a related field
  • Extensive hands-on experience with Python
  • Extensive hands-on experience with big-data frameworks
  • Extensive hands-on experience with AI/ML platforms
  • Proven expertise designing and managing large-scale data ecosystems and AI-driven data integration/mapping
  • Proficiency in front-end development for data visualization or user-centric tools
  • Familiarity with cloud platforms (AWS, Azure, or GCP) and modern data infrastructure
  • Strong leadership, communication, and strategic thinking skills for managing cross-functional teams
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The Company
HQ: London
5,017 Employees
Year Founded: 2007

What We Do

Photon.com has emerged as one of the world’s largest and fastest-growing Digital Agencies. We work with 40% of the Fortune 100 on their Digital initiatives and are known for our ability to integrate Strategy Consulting, Creative Design, and Technology at scale. Please visit www.photon.com to learn more about us, how we work, and our customer case studies. Digital Transformation Starts Here.

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