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Manager Machine Learning Finance Jobs in Alabama

... machine learning, and AI-driven insights, and enjoy working in a consulting environment where ... Support Professional Services and Managed Services initiatives as needed, ensuring billable ...

Enable future machine learning use cases by ensuring curated datasets are ML-ready, including ... Financial Planning and wellbeing - No matter what financial goals our employees have set, we want ...

Industry/Sector Not Applicable Specialism IFS - Information Technology (IT) Management Level ... Those in data science and machine learning engineering at PwC will focus on leveraging advanced ...

AI Engineer

Birmingham, AL · On-site

$50K - $112K/yr

... Management Information Systems, Information Technology - At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud ...

New

Develop and deploy machine learning, predictive analytics, and prescriptive analytics, including ... Present findings, prototypes, and recommendations to peers, managers, operators, and executives ...

Generative AI Strategist

Montevallo, AL

$119K - $154K/yr

... of generative AI and machine learning? Join us in driving strategic alignment, fostering ... Engage in discussions about intricate industry-specific concepts with executives, line managers ...

Showing results 41-60

Manager Machine Learning Finance information

What does a Manager of Machine Learning in Finance do?

A Manager of Machine Learning in Finance oversees teams that develop and implement machine learning models to solve financial problems, such as risk assessment, fraud detection, and algorithmic trading. They coordinate with data scientists, engineers, and business stakeholders to ensure models meet regulatory standards and align with company goals. Additionally, they are responsible for project management, mentoring team members, and staying updated with advancements in both finance and artificial intelligence.

What are the key skills and qualifications needed to thrive as a Manager of Machine Learning in Finance, and why are they important?

To thrive as a Manager of Machine Learning in Finance, you need strong expertise in machine learning, statistics, and financial analysis, typically supported by a relevant advanced degree and experience in both data science and finance. Familiarity with programming languages like Python or R, cloud platforms, and machine learning frameworks such as TensorFlow or Scikit-learn is essential, along with knowledge of regulatory compliance systems. Exceptional leadership, strategic thinking, and communication skills set top candidates apart by enabling effective team management and cross-functional collaboration. These skills and qualities are crucial to drive innovative solutions, ensure regulatory adherence, and deliver business value in a complex financial environment.

What is the difference between Manager Machine Learning Finance vs Data Scientist Finance?

AspectManager Machine Learning FinanceData Scientist Finance
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or Finance; certifications in machine learning or data analysisBachelor's or Master's in Data Science, Statistics, or related fields; often includes certifications in data analysis or programming
Work EnvironmentLeads teams, manages projects, collaborates with stakeholders in financeAnalyzes data, develops models, supports decision-making in finance teams
Employer & Industry UsageFinancial institutions, hedge funds, investment firmsFinancial firms, banks, fintech companies

The Manager Machine Learning Finance oversees teams and projects applying machine learning to finance problems, focusing on leadership and strategy. In contrast, Data Scientists in finance primarily analyze data and develop models to support financial decisions. Both roles require strong technical skills, but the manager role emphasizes team management and project oversight.

How does a Manager of Machine Learning in Finance typically collaborate with cross-functional teams?

A Manager of Machine Learning in Finance often works closely with data scientists, software engineers, financial analysts, and business stakeholders. They are responsible for translating business problems into machine learning solutions and ensuring models meet both technical and regulatory requirements. Regular meetings and clear communication are essential, as the manager must align team efforts with organizational goals, facilitate knowledge sharing, and integrate model outputs into financial decision-making processes. Collaboration also involves coordinating with IT for data infrastructure and with compliance teams to uphold data privacy standards.
What are the most commonly searched types of Machine Learning Finance jobs in Alabama? The most popular types of Machine Learning Finance jobs in Alabama are:
What cities in Alabama are hiring for Manager Machine Learning Finance jobs? Cities in Alabama with the most Manager Machine Learning Finance job openings:

Sr. Databricks Solutions Architect

ECS

Huntsville, AL • On-site

Full-time

Re-posted 24 days ago


Job description

Everforth ECS is seeking a Sr. Databricks Solutions Architect to join our team in Huntsville, Alabama. This position is contingent upon contract award.
ECS is seeking a skilled Sr. Databricks Solutions Architect to join our Professional Services team. This position is contingent upon contract award. If you are passionate about helping customers solve complex big data challenges, leveraging Databricks for advanced analytics, machine learning, and AI-driven insights, and enjoy working in a consulting environment where technical expertise meets client engagement, this role is for you.
As a member of our team, you will work with customers on short- to medium-term engagements, providing technical guidance, architecture, and hands-on support to ensure they maximize value from their Databricks environments. You will collaborate with clients to design, implement, and optimize data pipelines, analytics workflows, and machine learning solutions, all while delivering exceptional customer service and consulting expertise.
Responsibilities
  • Lead customer engagements to design, build, and optimize Databricks-based architectures for advanced analytics, data engineering, and machine learning workloads.
  • Develop scalable ETL/ELT pipelines and integrate with cloud platforms (AWS, Azure, or GCP).
  • Guide customers on data governance, security, and compliance best practices within Databricks environments.
  • Consult on architecture, reference implementations, and best practices for leveraging Delta Lake, Unity Catalog, MLflow, and related Databricks capabilities.
  • Assist customers with productionalizing data pipelines, machine learning workflows, and AI-driven applications.
  • Provide escalated technical support for customer operational issues and help troubleshoot complex platform or workflow challenges.
  • Collaborate with internal and Databricks teams, including Engineers, Architects, Project Managers, and Customer Success teams, to ensure engagement goals are met.
  • Document technical designs, architecture patterns, deployment procedures, and lessons learned.
  • Stay current on Databricks platform features, distributed computing trends, and emerging big data technologies.
  • Deliver solutions that improve performance, scalability, and operational efficiency while meeting customer business objectives.
  • Support Professional Services and Managed Services initiatives as needed, ensuring billable deliverables meet customer expectations.

  • US Top Secret Clearance required.
  • 7+ years of experience in Data Engineering.
  • 10+ years of consulting experience, preferably in data platform or analytics-focused engagements.
  • Completion of 6-8 hands-on projects with Databricks in production environments.
  • Proven experience with Databricks, including Spark, Delta Lake, MLflow, and cloud integration.
  • Strong proficiency in Python and/or SQL for data engineering and analytics.
  • Deep understanding of distributed computing concepts and Apache Spark runtime internals.
  • Hands-on experience designing and deploying end-to-end big data and machine learning solutions.
  • Familiarity with data modeling, performance tuning, and production-grade pipeline design.
  • Experience working directly with customers in a consulting or professional services capacity.
  • Ability to manage technical scope, timelines, and delivery while maintaining excellent customer communication.
  • Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent professional experience.
  • Willingness to travel up to 30% for customer engagements.