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Temporary Data Scientist Machine Learning Jobs in Providence, RI

Citizens

Principal Data Engineer - Neo4J

Johnston, RI · On-site

Partner with data science and analytics teams to enable graph-based feature engineering and machine learning integration Preferred Technical Expertise * Deep expertise in Neo4j platform capabilities ...

Citizens

Principal Data Engineer - Neo4J

Johnston, RI · On-site

Partner with data science and analytics teams to enable graph-based feature engineering and machine learning integration Preferred Technical Expertise * Deep expertise in Neo4j platform capabilities ...

Citizens

Principal Data Engineer - Neo4J

Johnston, RI · On-site

Partner with data science and analytics teams to enable graph-based feature engineering and machine learning integration Preferred Technical Expertise * Deep expertise in Neo4j platform capabilities ...

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All JobsData Scientist Machine Learning JobsTemporary Data Scientist Machine Learning JobsTemporary Data Scientist Machine Learning Jobs in Providence, RI

Temporary Data Scientist Machine Learning information

See Providence, RI salary details

$37.9K

$124K

$198.5K

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

As of Aug 15, 2026, the average yearly pay for temporary data scientist machine learning in Providence, RI is $123,994.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,500.00 and $137,400.00 per year, depending on experience, location, and employer.

What is the difference between Temporary Data Scientist Machine Learning vs Temporary Data Analyst?

AspectTemporary Data Scientist Machine LearningTemporary Data Analyst
Required CredentialsBachelor's/Master's in Data Science, Computer Science, or related fields; knowledge of ML algorithmsBachelor's in Statistics, Mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentProject-based, collaborative teams, tech-focused companiesBusiness units, reporting teams, data-driven departments
Employer & Industry UsageTech firms, finance, healthcare, e-commerceRetail, marketing, finance, consulting

Temporary Data Scientist Machine Learning roles focus on developing and deploying machine learning models, requiring advanced analytics skills. Temporary Data Analysts primarily interpret data, generate reports, and support decision-making. While both roles involve data handling, Data Scientists with ML expertise work on predictive modeling, whereas Data Analysts focus on descriptive analytics. The choice depends on the project needs and skill requirements.

What does a temporary data scientist specializing in machine learning do?

A Temporary Data Scientist specializing in Machine Learning is responsible for designing, building, and deploying machine learning models to analyze data and generate insights, but works on a contract or short-term basis. Their duties often include data preprocessing, model selection and validation, and communicating results to stakeholders. They may also be tasked with automating processes, cleaning large datasets, and collaborating with other teams to implement solutions. The temporary nature of the job means they often focus on specific projects or provide support during peak periods.

What are the key skills and qualifications needed to thrive as a temporary data scientist specializing in machine learning?

To thrive as a Temporary Data Scientist Machine Learning, you generally need a strong background in statistics, programming (Python or R), and experience with machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with data visualization tools (like Tableau), machine learning libraries (such as scikit-learn, TensorFlow, or PyTorch), and version control systems (e.g., Git) is typically required. Strong problem-solving abilities, adaptability, and effective communication are crucial soft skills for collaborating with teams and translating technical findings to stakeholders. These skills ensure that temporary data scientists can quickly contribute actionable insights, drive data-driven decisions, and add value within a limited time frame.

What are some typical projects or tasks a temporary data scientist specializing in machine learning might work on?

As a temporary Data Scientist focusing on machine learning, you can expect to work on short-term, high-impact projects such as building predictive models, cleaning and preparing data, or developing automated analytics solutions. You may be brought in to support ongoing initiatives, provide expertise for a specific project phase, or help accelerate a backlog of tasks. Collaboration is common, and you'll likely work closely with data engineers, business analysts, and domain experts to understand requirements and deliver actionable insights within tight deadlines. This role offers exposure to diverse datasets and tools, and is an excellent opportunity to rapidly expand your experience and network.

What are the most commonly searched types of Data Scientist Machine Learning jobs in Providence, RI?

The most popular types of Data Scientist Machine Learning jobs in Providence, RI are:

What are popular job titles related to Temporary Data Scientist Machine Learning jobs in Providence, RI?

For Temporary Data Scientist Machine Learning jobs in Providence, RI, the most frequently searched job titles are:

What job categories do people searching Temporary Data Scientist Machine Learning jobs in Providence, RI look for?

The top searched job categories for Temporary Data Scientist Machine Learning jobs in Providence, RI are:

Temporary Data Scientist Machine Learning jobs near you
Infographic showing various Temporary Data Scientist Machine Learning job openings in Providence, RI as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $123,994 per year, or $59.6 per hour.

Sr Data Engineer- Data Platform & AI Enablement

Citizens Bank

Johnston, RI • On-site

$115K - $138K/yr

Full-time

Re-posted 9 days ago

Job description


Senior Data Engineer - Enterprise Data Enablement
The Enterprise Data Enablement team is seeking a Senior Data Engineer who can design, develop, and maintain secure, scalable, and efficient data pipelines and platforms. This role will focus on building and deploying data solutions across financial consumer business domains by leveraging existing and new data framework capabilities to acquire, transform, stream, and integrate data. The candidate will also contribute to innovative data engineering solutions, including AI/GenAI/Agentic AI-ready data capabilities, while collaborating with and supporting a team of data engineers in building scalable, secure, and intelligent data platforms.
Primary Responsibilities
  • Design, build, and maintain reliable, efficient, and scalable data pipelines to acquire, transform, and store large datasets.
  • Develop robust data pipelines to collect, process, and compute metrics from various financial data sources while adhering to quality and development standards.
  • Contribute to application architecture and technical solutions, and help implement data framework patterns alongside senior engineers and architects.
  • Collaborate with cross-functional teams to deliver optimal data solutions that meet business and platform needs.
  • Develop and deploy high-quality, production-ready code.
  • Apply strong database design principles and data modeling techniques to translate business requirements into scalable data solutions.
  • Develop and optimize data models to support analytics, reporting, machine learning, and AI-driven use cases.
  • Support the implementation and enhancement of enterprise data frameworks and contribute to scalable solutions.
  • Identify opportunities to improve existing frameworks and help build reusable capabilities across the organization.
  • Troubleshoot and resolve data-related issues in a timely manner.
  • Execute unit testing for data pipelines, validate results, and ensure data quality and accuracy; partner with business users for User Acceptance Testing and support deployment activities.
  • Follow change management practices and ensure adherence to compliance and regulatory standards.
  • Design and build data pipelines and platform capabilities that support AI, Generative AI, and Agentic AI use cases, including model training, inference, retrieval, and orchestration workflows.
  • Enable AI-ready data foundations by developing high-quality, governed, and reusable datasets for machine learning, large language model (LLM), and intelligent automation solutions.
  • Develop and optimize pipelines for structured, semi-structured, and unstructured data to support GenAI use cases such as semantic search, document intelligence, and retrieval-augmented generation (RAG).
  • Partner with data scientists, ML engineers, architects, and product teams to integrate AI/GenAI capabilities into enterprise data platforms and workflows.
  • Implement metadata, lineage, governance, security, and access controls required for responsible AI and enterprise-scale GenAI adoption.
  • Ensure observability, reliability, performance, and data quality for data pipelines, including those supporting AI-enabled workflows.
Required Skills / Experience
  • 6-8+ years of experience in data engineering and distributed data processing technologies.
  • Hands-on experience with streaming technologies such as Apache Spark, Beam, or Flink.
  • Experience with message brokers such as Apache Kafka.
  • Experience working with microservices and batch processing systems.
  • Strong programming skills in Java and/or Scala; Python experience preferred.
  • Strong SQL development and performance optimization skills.
  • Solid knowledge of relational databases (Redshift, PostgreSQL, Snowflake) and NoSQL databases (MongoDB or similar).
  • Experience with CI/CD pipelines and version control systems such as Bitbucket and Git.
  • Experience with ETL development tools such as Talend or DataStage is a plus.
  • Experience with Java Spring Boot; familiarity with React, TypeScript, or Angular is a plus.
  • Understanding of cloud-based data processing, with AWS and/or Azure experience preferred.
  • Experience building data pipelines that support analytics, machine learning, and AI workloads.
  • Working knowledge of data engineering concepts supporting LLM-based applications, including retrieval pipelines, embeddings workflows, and unstructured data processing.
  • Familiarity with AI/GenAI concepts such as RAG, semantic search, document processing, and model inference workflows.
  • Understanding of data governance, security, lineage, and compliance requirements, particularly in regulated environments.
  • Exposure to workflow orchestration frameworks and automation patterns is a plus.
  • Exposure to vector databases, semantic models, or MLOps/LLMOps concepts is a plus.
  • Strong analytical and problem-solving skills, with the ability to collaborate effectively within technical teams.
Education, Certifications, and/or Other Professional Credentials
  • Bachelor's degree in Computer Science, Engineering, or a related technology field
Hours and Work Schedule
  • Hours per Week: 40
  • Work Schedule: Monday through Friday

About Us
Equal Employment Opportunity
Citizens, its parent, subsidiaries, and related companies (Citizens) provide equal employment and advancement opportunities to all colleagues and applicants for employment without regard to age, ancestry, color, citizenship, physical or mental disability, perceived disability or history or record of a disability, ethnicity, gender, gender identity or expression, genetic information, genetic characteristic, marital or domestic partner status, victim of domestic violence, family status/parenthood, medical condition, military or veteran status, national origin, pregnancy/childbirth/lactation, colleague's or a dependent's reproductive health decision making, race, religion, sex, sexual orientation, or any other category protected by federal, state and/or local laws. At Citizens, we are committed to fostering an inclusive culture that enables all colleagues to bring their best selves to work every day and everyone is expected to be treated with respect and professionalism. Employment decisions are based solely on merit, qualifications, performance and capability.
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Any offer of employment is conditioned upon the candidate successfully passing a background check, which may include initial credit, motor vehicle record, public record, prior employment verification, and criminal background checks. Results of the background check are individually reviewed based upon legal requirements imposed by our regulators and with consideration of the nature and gravity of the background history and the job offered. Any offer of employment will include further information.

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