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Entry Level Algorithm Jobs in California (NOW HIRING)

Sonsoft Inc

Java J2EE Engineer

Sunnyvale, CA · On-site

At least 1 years of experience with concepts of Core Java with data structure, algorithms, multi threading, OOPs concept and knowledge on Oracle DB. * At least 2 years of experience in software ...

Trinion America Inc.

AGV AMR technician

San Diego, CA · On-site

... and path planning algorithms (SLAM, A*, Dijkstra) · Proficient in diagnostics and debugging tools (serial monitor, network analyzers, etc.) · Bachelor's degree in Electrical, Electronics ...

Evolver

GenAI Engineer (Python)

Palo Alto, CA · On-site

Familiarity with fundamental concepts in data structures and algorithms. * Preferred: * Experience ... Competitive Entry-Level Salary: Reflecting your skills and potential. * Flexible Work: Work options ...

Conventus

Financial Engineer

Westlake Village, CA

$107K - $132K/yr

... algorithms when needed. * Create, maintain, deliver monthly and quarterly analytical reports ... Masters or MBA in related field preferred. * Entry level knowledge of residential mortgage industry ...

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Entry Level Algorithm information

What is an entry level algorithm job?

Entry level algorithm jobs are positions for individuals who are beginning their careers in developing, analyzing, and implementing algorithms. These roles typically involve tasks such as writing code, optimizing existing algorithms, and supporting senior engineers in solving computational problems. They are common in industries like software development, finance, and data science, and usually require a background in computer science or a related field. Candidates are expected to have foundational knowledge in programming languages and basic data structures and algorithms.

What are some typical projects or tasks an entry level algorithm engineer might work on during their first year?

As an entry-level algorithm engineer, you can expect to work on tasks such as implementing and optimizing basic algorithms, analyzing data to improve existing solutions, and collaborating with more senior engineers on larger projects. Common responsibilities include debugging code, running performance tests, and contributing to documentation. You may also be involved in team meetings to review algorithmic approaches and brainstorm new solutions, offering a valuable opportunity to learn from experienced colleagues and develop your technical skills.

What are the key skills and qualifications needed to thrive as an entry level algorithm engineer, and why are they important?

To thrive as an Entry Level Algorithm Engineer, you need a solid background in computer science fundamentals, mathematics (especially statistics and linear algebra), and programming, typically demonstrated by a relevant degree or coursework. Familiarity with coding languages like Python or C++, version control systems such as Git, and experience with algorithm analysis tools are commonly expected. Strong problem-solving abilities, attention to detail, and effective teamwork and communication skills help you stand out in this position. These skills and qualities are essential for efficiently designing, optimizing, and implementing algorithms that solve complex problems within collaborative engineering environments.

What is the difference between Entry Level Algorithm vs Data Analyst?

AspectEntry Level AlgorithmData Analyst
Required CredentialsBachelor's in CS, Math, or related field; basic programming skillsBachelor's in Statistics, Math, or related field; proficiency in data tools
Work EnvironmentTech companies, research labs, software development teamsBusiness, finance, healthcare, and marketing sectors
Industry UsageUsed in software development, AI, and machine learning projectsUsed for interpreting data, generating reports, and supporting decision-making

Entry Level Algorithm roles focus on developing and optimizing algorithms primarily in tech and research settings, requiring programming skills and mathematical knowledge. Data Analysts interpret data to help businesses make informed decisions, often using statistical tools. While both roles involve working with data and require similar educational backgrounds, their core functions and work environments differ significantly.

What are the most commonly searched types of Algorithm jobs in California?

The most popular types of Algorithm jobs in California are:

Entry Level Algorithm jobs near you
Infographic showing various Entry Level Algorithm job openings in California as of July 2026, with employment types broken down into 2% Locum Tenens, 72% Full Time, 21% Part Time, 1% Temporary, and 4% Contract. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution.

Data Scientist - Fraud Detection

Mountain View, CA • On-site

DataVisor
Software Development • 1 - 10 employees
Learn more about DataVisorexternal

Full-time

Medical, PTO

Posted 15 days ago

Key responsibilities

  • Develop and deploy machine learning models for fraud detection and risk assessment.

  • Perform exploratory data analysis (EDA) to identify trends, anomalies, and patterns in transactional data.

  • Collaborate with engineering and business teams to integrate ML models into production systems.

Job description

About DataVisor:
DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!

Position Overview:

We are looking for a motivated Entry-Level Data Scientist to join our Fraud Detection team. In this role, you will leverage your machine learning and data analysis skills to identify fraudulent activities, build predictive models, and uncover hidden patterns in large datasets. You will work closely with cross-functional teams to develop scalable solutions that enhance our fraud detection capabilities. This is a great opportunity to grow your skills in a fast-paced, data-driven environment while making a real impact in the fight against fraud.

Key Responsibilities:

  • Develop and deploy machine learning models for fraud detection and risk assessment.
  • Perform exploratory data analysis (EDA) to identify trends, anomalies, and patterns in transactional data.
  • Clean, preprocess, and analyze large datasets using Python and popular data science libraries (pandas, NumPy, scikit-learn, etc.).
  • Collaborate with engineering and business teams to integrate ML models into production systems.
  • Continuously monitor model performance and refine algorithms to improve accuracy.
  • Stay updated with the latest advancements in fraud detection techniques and ML/AI technologies.

Requirements

  • Master’s degree in Computer Science, Data Science, Statistics, or a related quantitative field. Ph.D. degree is a plus. 
  • Strong programming skills in Python and familiarity with data science libraries (NumPy, Pandas, scikit-learn, TensorFlow/PyTorch is a plus).
  • Solid understanding of machine learning algorithms (supervised/unsupervised learning, anomaly detection, classification, etc.).
  • Experience with SQL and data manipulation/analysis in large datasets.
  • Strong problem-solving skills and patience for deep-dive data exploration.
  • Prior internship or project experience in fraud modeling, risk analysis, or related fields is a plus.
  • Excellent communication skills and ability to work in a collaborative environment.
Nice to have
  • Familiarity with big data tools (Spark, Hadoop, Dask).
  • Knowledge of graph-based fraud detection techniques.
  • Experience with cloud platforms (AWS, GCP, Azure).

Benefits

PTO, Stock Option, Health Benefits