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Machine Learning Consultant Jobs in California (NOW HIRING)

1872 Consulting

Senior ML Engineer

San Francisco, CA · On-site

$123K - $169K/yr

Job Summary : 1872 Consulting is a company focused on engineering solutions, and they are seeking a Senior ML Engineer to enhance their machine learning capabilities. The role involves training ...

Matroid

Deep Learning Field Engineer

Palo Alto, CA · On-site

... machine learning, or related fields. • Previous work experience in field engineering, professional services, consulting, or another customer-facing field. • Experience with high growth technology ...

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Machine Learning Consultant information

What does a machine learning consultant do?

A Machine Learning Consultant helps businesses implement AI and machine learning solutions to improve decision-making, automate processes, and enhance efficiency. They analyze data, develop models, and provide strategic recommendations tailored to a company's needs. Their role often involves working with engineers, data scientists, and stakeholders to integrate machine learning into existing systems.

What are the key skills and qualifications needed to thrive as a machine learning consultant?

To thrive as a Machine Learning Consultant, you need strong expertise in machine learning algorithms, data analysis, and programming skills—typically supported by a degree in computer science, statistics, or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and cloud platforms, as well as relevant certifications like AWS Certified Machine Learning or TensorFlow Developer, are highly beneficial. Excellent communication, problem-solving, and project management skills help consultants translate complex technical concepts for clients and efficiently drive projects. These skills and qualities are crucial for delivering effective machine learning solutions that address business needs and foster client trust.

What does a typical project look like for a machine learning consultant?

As a Machine Learning Consultant, your projects often begin with collaborating closely with clients to understand their data challenges and business goals. You might be tasked with activities such as setting up data pipelines, selecting and training machine learning models, and presenting actionable insights. Projects can vary in length and scope, ranging from short-term advisory engagements to long-term implementations, and often involve teamwork with data engineers, business analysts, and IT departments. This dynamic environment means you need to balance hands-on technical work with client-facing responsibilities, ensuring successful delivery and measurable impact.

How much does a machine learning consultant make?

A machine learning consultant's salary varies based on experience, location, and industry, but typically ranges from $80,000 to $150,000 annually. Senior consultants with specialized skills in data modeling and programming can earn higher salaries, especially when working on complex projects or with advanced tools like Python and TensorFlow.

What are the most commonly searched types of Machine Learning Consultant jobs in California?

The most popular types of Machine Learning Consultant jobs in California are:

What are popular job titles related to Machine Learning Consultant jobs in California?

For Machine Learning Consultant jobs in California, the most frequently searched job titles are:

What job categories do people searching Machine Learning Consultant jobs in California look for?

The top searched job categories for Machine Learning Consultant jobs in California are:

What cities in California are hiring for Machine Learning Consultant jobs?

Cities in California with the most Machine Learning Consultant job openings:

Machine Learning Consultant jobs near you
Infographic showing various Machine Learning Consultant job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Modeling Lead - Credit Modeling

San Francisco, CA • On-site

ExlService Holdings, Inc.
IT Services • 10K+ employees
Learn more about ExlService Holdings, Inc.external

$202K - $280K/yr

Full-time

Re-posted yesterday

ExlService Holdings rating

7.8

Company rating: 7.8 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

Job description


We are seeking a highly skilled and experienced Machine Learning Modeling Lead - Credit Modeling to join our ML Model Innovation team within the banking/fintech domain (digital lending). The ideal candidate will be responsible for leading the development and deployment of machine learning models that power key business decisions such as collections models, credit risk scoring, fraud detection, customer segmentation, and personalized financial services.
The Individual needs to have strong knowledge of banking business, data and domain across the customer lifecycle as well as bureau data. They will collaborate with cross-functional teams and provide technical leadership to junior ML modelers and data scientists.
Key Roles and Responsibilities -
  • Lead end-to-end ML solution development & innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring.
  • Develop robust models which can drive business benefits. Support and review junior scientist submissions and share enhancement suggestions
  • Responsible for documentation/documentation reviews, model reviews and submission
  • Responsible for managing queries raised by Validation teams for the model
  • Collaborate with implementation teams to deploy models into production environments (cloud or on premises).
  • Work closely with business stakeholders to translate banking domain challenges into data-driven solutions.
  • Guide junior data scientists and engineers on best practices in model development
  • Continuously evaluate new tools, technologies, and frameworks relevant to ML in finance.
  • Publish internal research and promote a culture of innovation and experimentation.

Candidate Profile:
  • Strong business knowledge of banking analytics across the retail banking customer lifecycle.
  • 12+ years of experience in applied machine learning model development in the banking or financial services domain.
  • Hands-on experience leading ML projects and teams.
  • Strong experience with model development, deployment and monitoring in production environments.
  • Familiarity with collections, underwriting, fraud and ethical considerations in banking ML models.
  • Demonstrable leadership ability, superior problem solving and people management skills
  • Master's or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field

Skills:
  • Expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.).
  • Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.
  • Strong grasp of statistical modeling, optimization, and deep learning techniques.
  • Excellent communication skills and ability to explain complex concepts to non-technical stakeholders.

What we offer:
  • EXL Analytics offers an exciting, fast paced and innovative environment, which brings together a group of sharp and entrepreneurial professionals who are eager to influence business decisions. From your very first day, you get an opportunity to work closely with highly experienced, world class analytics consultants.
  • You will learn effective teamwork and time-management skills - key aspects for personal and professional growth
  • Analytics requires different skill sets at different levels within the organization. At EXL Analytics, we invest heavily in training you in all aspects of analytics as well as in leading analytical tools and techniques.
  • We provide guidance/ coaching to every employee through our mentoring program wherein every junior level employee is assigned a senior level professional as advisors.
  • Sky is the limit for our team members. The unique experiences gathered at EXL Analytics sets the stage for further growth and development in our company and beyond.

The typical base pay range for this role across the U.S. is USD $202,000 - $280,000 per year.
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits
The posted range is the hiring range for this role - a subset of the broader range available to employees over time - and reflects base salary across our national hiring scale.
Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position.
The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.
Responsibilities
  • Lead end-to-end ML solution development & innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring.
  • Develop robust models which can drive business benefits. Support and review junior scientist submissions and share enhancement suggestions
  • Responsible for documentation/documentation reviews, model reviews and submission
  • Responsible for managing queries raised by Validation teams for the model
  • Collaborate with implementation teams to deploy models into production environments (cloud or on-premises).
  • Work closely with business stakeholders to translate banking domain challenges into data-driven solutions.
  • Guide junior data scientists and engineers on best practices in model development
  • Continuously evaluate new tools, technologies, and frameworks relevant to ML in finance.
  • Publish internal research and promote a culture of innovation and experimentation.

Qualifications
Candidate Requirements:
  • Strong business knowledge of banking analytics across the retail banking customer lifecycle.
  • 12+ years of experience in applied machine learning model development in the banking or financial services domain.
  • Hands-on experience leading ML projects and teams.
  • Strong experience with model development, deployment and monitoring in production environments.
  • Familiarity with collections, underwriting, fraud and ethical considerations in banking ML models.
  • Demonstrable leadership ability, superior problem solving and people management skills
  • Master's or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field

Skills:
  • Expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.).
  • Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.
  • Strong grasp of statistical modeling, optimization, and deep learning techniques.
  • Excellent communication skills and ability to explain complex concepts to non-technical stakeholders.

What we offer:
  • EXL Analytics offers an exciting, fast paced and innovative environment, which brings together a group of sharp and entrepreneurial professionals who are eager to influence business decisions. From your very first day, you get an opportunity to work closely with highly experienced, world class analytics consultants.
  • You will learn effective teamwork and time-management skills - key aspects for personal and professional growth
  • Analytics requires different skill sets at different levels within the organization. At EXL Analytics, we invest heavily in training you in all aspects of analytics as well as in leading analytical tools and techniques.
  • We provide guidance/ coaching to every employee through our mentoring program wherein every junior level employee is assigned a senior level professional as advisors.
  • Sky is the limit for our team members. The unique experiences gathered at EXL Analytics sets the stage for further growth and development in our company and beyond.

The typical base pay range for this role across the U.S. is USD $200,000 - $280,000 per year.
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits
The posted range is the hiring range for this role - a subset of the broader range available to employees over time - and reflects base salary across our national hiring scale.
Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position.
The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.

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