
ML / Applied Scientist
Remote · USJob$115–130K/yrSeen todaySeen in employer's feed today
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Job overview
The ML / Applied Scientist develops machine learning models for entity resolution, inference, and intelligent data classification across enterprise data ecosystems. The role works with architects, data domain SMEs, backend engineers, and platform stakeholders to build AI solutions that improve data quality and business intelligence. It is remote and open to qualified applicants in the United States.
Skills & qualifications
Skills
Qualifications
Benefits
Full job description
About the role:
As an ML / Applied Scientist, you will make an impact by developing machine learning models that power entity resolution, inference, and intelligent data classification across enterprise data ecosystems. You will be a valued member of the AI and Data Engineering team, working closely with Architects, Data Domain SMEs, Backend Engineers, and platform stakeholders to build scalable AI solutions that improve data quality and business intelligence.
In this role, you will:
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Design and develop entity matching models that combine fuzzy and exact matching techniques to create and maintain trusted golden records.
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Build machine learning models that identify patterns, relationships, and anomalies across complex enterprise datasets to support data profiling and business insights.
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Develop AI-powered labeling and classification solutions using Gemini and other large language model technologies to support business ontology initiatives.
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Deploy, monitor, evaluate, and continuously improve machine learning models on Vertex AI, ensuring high levels of accuracy, performance, and reliability.
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Partner with cross-functional teams to integrate AI capabilities into enterprise platforms, workflows, and agent orchestration solutions.
Work model:
We strive to provide flexibility wherever possible. Based on this role’s business requirements, this is a remote position open to qualified applicants in the United States. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs.
The working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.
What you need to have to be considered:
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5+ years of experience in applied machine learning, with expertise in entity resolution, classification, NLP, or matching systems.
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Strong programming skills in Python and hands-on experience with machine learning frameworks such as PyTorch and/or scikit-learn.
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Experience deploying, monitoring, and evaluating machine learning models in Vertex AI or similar cloud ML platforms.
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Experience working with embeddings, large language models (LLMs), and NLP techniques for classification, search, matching, or inference use cases.
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Proven experience designing labeling and training workflows using weak supervision, human-in-the-loop review processes, and AI-assisted labeling approaches.
These will help you stand out:
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Experience working with knowledge graphs, ontologies, or graph-based machine learning applications.
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Understanding of MLOps practices, including model versioning, monitoring, drift detection, and automated retraining pipelines.
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Experience building enterprise AI solutions using Google Cloud Platform services, including BigQuery and Vertex AI.
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Familiarity with Gemini APIs and applying generative AI technologies in production environments.
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Google Cloud Professional Machine Learning Engineer certification.
We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role.
Salary and Other Compensation :
Applications will be accepted until November 29, 2026
The annual salary for this position is between $ 115,000 - $ 130,000 depending on experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.
Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
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Medical/Dental/Vision/Life Insurance
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Paid holidays plus Paid Time Off
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401(k) plan and contributions
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Long-term/Short-term Disability
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Paid Parental Leave
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Employee Stock Purchase Plan
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Cognizant is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.
How this job compares
Set against the 585 open Machine Learning Scientist jobs on Olive Jobs, counted November 10, 2026.
- Pay
- This posting$115–130K/yr
- US median$158–249K/yr538 salary ranges
This range's midpoint, $123K, is 38% below the US median midpoint ($197K) — in the lowest quarter of Machine Learning Scientist salary ranges across the US.
- Openings
- Olive Jobs lists 585 open Machine Learning Scientist jobs across the US. 4% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 5 years of experience; Machine Learning Scientist postings that state a minimum ask for 5 years at the median (493 postings).
- Education
- 125 of the 585 open Machine Learning Scientist jobs (21%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 10, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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