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Statistical Learning Jobs in California (NOW HIRING)

InMobi Advertising

Applied Scientist III

San Mateo, CA · On-site

Statistical learning theory, optimization, probability theory, and information theory; Causal inference, decision theory, game theory; Online learning, bandits, RL, Bayesian methods. • Strong ...

InMobi Advertising

Applied Scientist III

San Mateo, CA · On-site

Statistical learning theory, optimization, probability theory, and information theory; Causal inference, decision theory, game theory; Online learning, bandits, RL, Bayesian methods. • Proficient ...

Guidewire Software

ML Platform Engineer

San Mateo, CA

$124K - $210K/yr

Summary As an ML Platform Engineer, you'll help build and evolve the infrastructure that enables machine learning teams to develop, deploy, and operate models efficiently at scale. You'll work ...

Los Angeles Kings

LA Kings - Sr. Data Engineer

El Segundo, CA · On-site

$122K - $146K/yr

... statistical learning, and machine learning Qualifications : Required : • BA/BS Degree (4-year) Computer Science, Data Science, or similar field • 4-6 years related work experience • Expert ...

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All JobsStatistical Learning JobsStatistical Learning Jobs in California

Statistical Learning information

What are the key skills and qualifications needed to thrive as a Statistical Learning Specialist, and why are they important?

To thrive as a Statistical Learning Specialist, you need a strong background in statistics, probability, and machine learning, typically supported by an advanced degree in statistics, mathematics, computer science, or a related field. Expertise with programming languages such as Python or R, experience with statistical software (e.g., SAS, MATLAB), and familiarity with data analysis libraries are essential. Critical thinking, problem-solving, and effective communication skills help translate complex data insights into actionable business strategies. These competencies are crucial for extracting meaningful patterns from data and driving data-informed decision-making.

What is statistical learning?

Statistical learning is a field within data analysis and machine learning that focuses on understanding and modeling the relationship between variables using statistical methods. It involves techniques such as regression, classification, and pattern recognition, often utilizing tools like R or Python. Professionals in this area analyze data to make predictions or inform decisions based on statistical models.

What jobs make $1,000,000 a year?

In the field of statistical learning, high-paying roles such as data science executives, chief data officers, or senior machine learning engineers can reach or exceed $1,000,000 annually, especially in large tech companies or finance firms. These positions typically require advanced skills in statistical modeling, programming, and experience managing large datasets, often combined with performance-based bonuses and stock options.

What careers can you get with statistics?

A career in statistical learning can lead to roles such as data analyst, data scientist, statistician, machine learning engineer, and quantitative researcher. These positions typically require skills in programming, data analysis, and statistical software, and are common in industries like finance, healthcare, technology, and government. Certifications in data analysis or machine learning can enhance job prospects.

How do professionals in statistical learning typically collaborate with data scientists and domain experts on projects?

Professionals in statistical learning often work closely with data scientists and domain experts to ensure that the models they develop are both statistically sound and practically relevant. Collaboration usually involves joint problem definition, sharing data insights, and iterative feedback on model performance. Statistical learning experts contribute their knowledge of algorithms and statistical methods, while data scientists handle data pre-processing and engineering, and domain experts provide context to interpret results. This multidisciplinary teamwork helps ensure that solutions are robust and actionable for stakeholders.

Is 40 too late for data science?

Statistical learning roles in data science do not have strict age limits, and many professionals start or transition into the field later in life. Success depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be developed through online courses, certifications, and practical experience regardless of age.

What is the difference between Statistical Learning vs Data Analyst?

AspectStatistical LearningData Analyst
Required CredentialsDegree in Statistics, Data Science, or related fieldsDegree in Statistics, Data Science, Business, or related fields
Work EnvironmentResearch, academia, tech companies, data science teamsBusiness, marketing, finance, healthcare organizations
Employer & Industry UsageTech firms, research institutions, startupsCorporations, consulting firms, government agencies
Common Search & ComparisonStatistical Learning vs Data Analyst

Statistical Learning focuses on developing models and algorithms to understand data patterns, often requiring advanced statistical and programming skills. Data Analysts interpret data to generate reports and insights, typically emphasizing data visualization and business understanding. While both roles analyze data, Statistical Learning is more research-oriented and technical, whereas Data Analysts focus on practical data interpretation for decision-making.

What are popular job titles related to Statistical Learning jobs in California? For Statistical Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Statistical Learning jobs in California look for? The top searched job categories for Statistical Learning jobs in California are:
What cities in California are hiring for Statistical Learning jobs? Cities in California with the most Statistical Learning job openings:
Statistical Learning jobs near you

Senior Manager, Machine Learning

Gridware

San Francisco, CA • On-site

Full-time

Posted 22 days ago

Job description

Job Summary:
Gridware is a San Francisco-based technology company dedicated to protecting and enhancing the electrical grid. As a Senior ML Manager, you will lead the cloud-based modeling team, focusing on team management, strategic initiatives, and best practices for high throughput machine learning.
Responsibilities:
• 70% team management and strategy, 30% IC time
• Grow a high-performing team through hiring, coaching, performance management, and career development
• Partner with software engineering and ML infrastructure teams to ship robust, production-grade ML systems
• Own technical roadmap and execution for the cloud ML organization, balancing near-term product delivery with long-term technical investments
• Lead technical design of algorithms that improve the speed, accuracy, and reliability of Gridware’s automated hazard detection systems
• Define data strategy and labeling requirements, including real-world data collection and synthetic data generation approaches
• Explore and evaluate novel modeling approaches and research ideas to address existing and emerging automation challenges
Qualifications:
Required:
• 3+ years of direct management experience
• 8+ years of experience building and deploying machine learning models in production environments
• Experience with both deep learning and classical machine/statistical learning techniques
• Strong programming skills with demonstrated proficiency in Python
• Experience working within modern software stacks, including cloud platforms, containerization, and CI/CD workflows
Preferred:
• Experience working with signal processing or sensor-derived data
• Experience at a growth-stage start-up
• Familiarity with human-in-the-loop or human-on-the-loop ML systems
Company:
Gridware is a cybersecurity consultancy firm with decades of experience leading cybersecurity transformations and preventing cyber attacks. Founded in 2016, the company is headquartered in Sydney, AUS, with a team of 11-50 employees. The company is currently Early Stage.

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