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On Call Data Scientist Machine Learning Jobs (NOW HIRING)

Why this Role is Different Most Data Science roles currently on the market are focused on optimizing ad clicks or slightly improving recommendation engines. This isn't that. At Nelo, your models are ...

This is an exciting Senior Data Scientist/Machine Learning opportunity to have a real impact and be a large fish in a small pond! As a Senior Data Scientist at, you will: * Develop natural language ...

Data Scientist / Machine Learning Engineer, GenAI We are not accepting C2C or 1099 arrangements. Location: Charlotte, NC or Irving, TX Work Model: Hybrid (3 days onsite per week) Duration: 12-month ...

Analyze large and complex datasets to identify patterns, build statistical and machine learning ... Integrate the latest data science innovations into product solutions, enhancing data, analytical ...

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On Call Data Scientist Machine Learning information

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$37.5K

$122.7K

$196.5K

How much do on call data scientist machine learning jobs pay per year?

As of Jul 7, 2026, the average yearly pay for on call data scientist machine learning in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as the Pareto principle, suggests that roughly 80% of the results come from 20% of the efforts or features. Data scientists often use this concept to focus on the most impactful variables, optimize models, and prioritize tasks for efficiency in machine learning projects.

Is 40 too late for data science?

Age is not a barrier to becoming a data scientist or working in machine learning. Many professionals transition into data science later in their careers by acquiring relevant skills such as programming, statistics, and machine learning tools. Continuous learning and practical experience are key factors for success in the field regardless of age.

What is the difference between On Call Data Scientist Machine Learning vs Data Scientist?

AspectOn Call Data Scientist Machine LearningData Scientist
CredentialsTypically requires a master's or PhD in data science, computer science, or related fields, with expertise in machine learningSimilar educational background, often with broader data analysis skills
Work EnvironmentOn-call basis, often in fast-paced settings, providing immediate solutions for machine learning issuesStandard office environment, focusing on data analysis, modeling, and reporting
Industry UsageCommon in tech, finance, healthcare where real-time machine learning support is neededWidespread across industries for data analysis and modeling tasks

While both roles require strong data science skills, the On Call Data Scientist Machine Learning specializes in providing immediate, on-demand support for machine learning systems, often in critical environments. A Data Scientist has a broader focus on data analysis, modeling, and insights without the immediate on-call requirement.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and deploy AI models, and while AI automation tools can assist with certain tasks, MLEs are essential for creating complex, customized solutions and maintaining model performance. AI may automate some routine aspects, but human expertise remains critical for model development, troubleshooting, and ethical considerations in the field.

Which 5 jobs will survive AI?

For an On Call Data Scientist Machine Learning role, jobs that involve complex problem-solving, creativity, and human judgment are more likely to survive AI automation. These include roles like data scientists, machine learning engineers, AI ethics specialists, cybersecurity analysts, and healthcare data analysts. Skills in critical thinking, domain expertise, and adaptability will remain valuable as AI tools continue to evolve.
What cities are hiring for On Call Data Scientist Machine Learning jobs? Cities with the most On Call Data Scientist Machine Learning job openings:
What are the most commonly searched types of Data Scientist Machine Learning jobs? The most popular types of Data Scientist Machine Learning jobs are:
What states have the most On Call Data Scientist Machine Learning jobs? States with the most job openings for On Call Data Scientist Machine Learning jobs include:
Data Scientist - Machine Learning Practitioner

Data Scientist - Machine Learning Practitioner

BlueConduit

Ann Arbor, MI • On-site, Remote

$140K - $150K/yr

Full-time

Medical, Dental, Vision, Retirement

Posted 20 days ago


Job description

Company overview

BlueConduit is an infrastructure analytics SaaS company and social enterprise helping communities make better, faster, and more equitable decisions about critical water infrastructure. Our founding team pioneered predictive modeling for lead service line replacement in Flint, Michigan, and BlueConduit now works with hundreds of cities and utilities across North America.

Our platform helps utilities, municipalities, government agencies, and consultants combine fragmented infrastructure records, field observations, geospatial data, and predictive models to identify risk, prioritize work, meet compliance requirements, and communicate clearly with the public. We are a remote-first team committed to using data science for social good and building tools that are trusted by the people making high-stakes infrastructure decisions.

The role

BlueConduit is hiring a Data Scientist to improve and expand the machine learning models at the core of our infrastructure analytics platform. In this role, you will work on models that help cities prioritize infrastructure investments, reduce risk, and improve drinking water outcomes. You will strengthen our existing modeling workflows, help launch new model products and asset classes, and communicate results clearly to both technical and nontechnical audiences.

This is a strong fit for someone who combines rigorous applied ML judgment with product-minded execution: you enjoy messy real-world data, care about model validation and uncertainty, can build repeatable workflows rather than one-off analyses, and like explaining technical work to people who need to act on it.

In this role you will be expected to be using the latest available AI tools to code productively. You will need to understand what you're building and coding, and understand agentic AI workflows that involve best practices, including unit tests, built-in code reviews, and extensive documentation in your commits for fellow data scientists and software engineers.

This role reports to the VP of Data Science.

What you'll do
  • Build, validate, and improve machine learning and statistical models used in BlueConduit's infrastructure analytics products
  • Help design, build, and launch new model products and model classes that broaden the assets and risks BlueConduit can predict
  • Improve data science workflows, model evaluation, reproducibility, and handoffs into software/product systems
  • Work with heterogeneous municipal, infrastructure, geospatial, and field-observation datasets to generate actionable risk predictions
  • Design validation approaches and communicate model uncertainty, limitations, and tradeoffs clearly to internal teams and customers
  • Use modern AI coding tools such as Claude Code, Codex, or similar systems to accelerate development while applying strong independent programming judgment
  • Use multiple AI agents to contribute to extremely robust workflows and code pipelines with built-in testing and reviews
  • Support customer-facing analysis and present findings in ways that are clear, accurate, and useful for nontechnical decision-makers
  • Contribute to R&D that scales the impact, reliability, and reach of BlueConduit's predictive methods

BlueConduit is a small, remote, and growing team, so this is an opportunity to shape both the role and the next generation of our data science products.

What we're looking for
  • Strong Python-based data science experience, including pandas, NumPy, scikit-learn, and production-quality analysis workflows
  • An undergraduate degree in a quantitative field (e.g., CS, math, stats, physics)
  • Experience building, validating, and improving machine learning or statistical models on messy real-world data
  • Experience building repeatable data science workflows in a product at a SaaS company or similarly operational environment
  • Ability to communicate modeling results, uncertainty, and tradeoffs clearly to technical and nontechnical stakeholders
  • Fluency using modern AI coding tools - including coordinating work of AI agents - to accelerate development, grounded in strong independent programming ability and judgment
  • Strong data visualization, verbal communication, and written communication skills
  • Comfort with Git-based development workflows
  • Attention to detail, curiosity, and commitment to building models that are understandable, usable, and trusted by the people making infrastructure decisions
  • Passion for socially impactful data science, environmental justice, and public-interest technology
We're especially interested in candidates with one or more of the following
  • A rigorous graduate degree in a quantitative field, or equivalent applied experience
  • Experience modeling asset classes beyond BlueConduit's current water distribution portfolio, such as fire risk, wastewater, hydraulic systems, climate risk, insurance risk, or other infrastructure domains
  • Experience with geospatial data, GIS systems, GeoPandas, or spatial modeling
  • Experience creating a new model product or extending an existing model product to a new domain or asset class
  • Experience with both global/cross-location models and local/site-specific models
  • Experience with methodologies beyond classical ML, such as neural networks, transformers, transfer learning, or other modern ML approaches
  • Experience with cloud-based model workflows, model tracking, versioning, Databricks, PySpark, or distributed computing
  • Familiarity with infrastructure, water quality, government data, or regulated public-sector decision environments
  • Experience working in Agile product development environments
  • Aptitude and interest in building with rapid iteration cycles involving prototyping, receiving feedback, and rebuilding
Location

Remote

Compensation
  • Expected salary range: $140,000-$150,000, commensurate with experience
  • Equity options
  • Health, vision and dental benefits
  • Simple IRA benefit with company contribution matching

Every qualified applicant will receive consideration for employment without regard to race, age, color, religion, sex, sexual orientation, or national origin.

Employment Type: FULL_TIME