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On Call Data Scientist Machine Learning Jobs (NOW HIRING)

Koantek

Data Scientist

Chandler, AZ · On-site

Data Scientist / Senior Data Scientist We are seeking a highly skilled Data Scientist with strong ... Build and optimize machine learning models for classification, regression, predictive analytics ...

Koantek

Data Scientist

Chandler, AZ · On-site

Data Scientist / Senior Data Scientist We are seeking a highly skilled Data Scientist with strong ... Build and optimize machine learning models for classification, regression, predictive analytics ...

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All JobsOn Call Data Scientist Machine Learning Jobs

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:
On Call Data Scientist Machine Learning jobs near you
Machine Learning Engineer / Data Scientist

Machine Learning Engineer / Data Scientist

Fusemachines

New York, NY • On-site, Remote

$100K - $120K/yr

Full-time

Posted 11 days ago

Job description

About Fusemachines
Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients' AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
Type: Full-time, RemoteRole Overview
We're hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You'll work across the ML lifecycle-from problem framing and data exploration to model development, evaluation, deployment, and monitoring-often in partnership with client stakeholders and internal delivery teams.
You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems.Key Responsibilities
  • Problem Framing & Stakeholder Partnership
    • Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.).
    • Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability).
  • Data Analysis & Feature Engineering
    • Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses.
    • Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices.
    • Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions.
  • Model Development (Core ML)
    • Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data).
    • Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation.
    • Build time series models (statistical and ML/DL approaches) and validate with proper backtesting.
    • Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness.
    • Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making.
  • Deep Learning
    • Build and train deep learning models using PyTorch or TensorFlow/Keras.
    • Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design).
  • Evaluation, Explainability, and Iteration
    • Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports.
    • Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence.
  • Productionization & MLOps (Project-Dependent)
    • Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration.
    • Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans.
  • Documentation & Communication
    • Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders.
    • Create documentation and lightweight demos that make results actionable.
Success in This Role Looks Like
  • You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency).
  • Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring.
  • Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly.
Required Qualifications
  • 3-8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior).
  • Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent).
  • Strong SQL skills (joins, window functions, aggregation, performance awareness).
  • Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset.
  • Hands-on experience across multiple model types, including:
    • Classification & regression
    • Time series forecasting
    • Clustering/segmentation
  • Experience with deep learning in PyTorch or TensorFlow/Keras.
  • Strong problem-solving skills: ability to work with ambiguous goals and messy data.
  • Clear communication skills and ability to translate analysis into decisions.
Preferred Qualifications
  • Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows).
  • Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining).
  • Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks).
  • Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker).
  • Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls).
  • Consulting or client-facing delivery experience.

Certifications (Strong Plus)
Candidates with at least one relevant certification are especially encouraged to apply:
  • Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks)
  • Databricks certifications (Data Scientist, Data Engineer, or related)
Nice-to-Have
  • Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests).
  • Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products.
  • Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability.

Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
Important: Immigration Sponsorship Policy
Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including:
  • Direct Company Sponsorship: Such as H-1B, J-1, or TN visas.
  • Employer of Record: Listing Fusemachines as the immigration employer on any government documentation.
  • Written Documentation: Providing letters or other support for any work authorization (e.g., OPT, STEM OPT, CPT).

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