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Full Time Data Scientist Machine Learning Jobs (NOW HIRING)

As an Advance Data Scientist, you will join a high-performing, global team, and be responsible for ... Minimum of 4 years of full time Machine Learning experience applied on top of processes, systems ...

As an Advance Data Scientist, you will join a high-performing, global team, and be responsible for ... Minimum of 4 years of full time Machine Learning experience applied on top of processes, systems ...

As an Advance Data Scientist, you will join a high-performing, global team, and be responsible for ... Minimum of 4 years of full time Machine Learning experience applied on top of processes, systems ...

Data Scientist Job Location: Detroit, MI (Hybrid) Job Type: Contract ... Develop and deploy Machine Learning and AI models. * Analyze large datasets and generate business ...

Create sustainable data science infrastructure and adheres to data analysis/machine learning best ... practices. Perform data cleaning, quality control, exploratory data analysis to gauge the need for ...

Key Responsibilities The Senior Data Scientist applies strong expertise in machine learning, data science, and other artificial intelligence techniques to design, prototype and build next generation ...

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and ... Full time/Part time Full time Pay Basis Salary More Information: * Please visit "Why Carnegie ...

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Full Time Data Scientist Machine Learning information

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$37.5K

$122.7K

$196.5K

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

As of Jul 12, 2026, the average yearly pay for full time 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 are some common challenges faced by full-time Data Scientists specializing in Machine Learning, and how can they be addressed?

Full-time Data Scientists in Machine Learning often encounter challenges such as dealing with messy or incomplete data, tuning complex models for optimal performance, and effectively communicating technical insights to non-technical stakeholders. Addressing these challenges usually involves collaborating closely with data engineers to improve data quality, staying updated with the latest ML techniques, and developing strong communication skills to translate findings into actionable business strategies. Additionally, regular code reviews and participation in cross-functional meetings help ensure alignment and foster a supportive team environment.

What does a Full Time Data Scientist specializing in Machine Learning do?

A Full Time Data Scientist specializing in Machine Learning is responsible for analyzing large datasets to discover patterns and insights, and for building, testing, and deploying machine learning models to solve business problems. They use statistical techniques, programming skills, and domain knowledge to turn raw data into actionable information. Their day-to-day tasks often include data cleaning, feature engineering, model selection, and performance evaluation. They also collaborate with other teams to integrate machine learning solutions into products or decision-making processes. This role typically requires proficiency in languages like Python or R, and familiarity with tools such as TensorFlow, scikit-learn, or PyTorch.

What are the key skills and qualifications needed to thrive as a Full Time Data Scientist Machine Learning, and why are they important?

To thrive as a Full Time Data Scientist Machine Learning, you need strong analytical skills, expertise in statistics, machine learning techniques, and a relevant degree in computer science, mathematics, or a related field. Proficiency with programming languages such as Python or R, experience with machine learning libraries like TensorFlow or scikit-learn, and familiarity with data visualization and big data platforms are typically required. Critical thinking, problem-solving abilities, and effective communication are essential soft skills for collaborating with stakeholders and translating data insights into business value. These skills are crucial for developing robust models, interpreting complex data, and driving impactful, data-driven decisions within organizations.

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

AspectFull Time Data Scientist Machine LearningData Analyst
Required CredentialsBachelor's/Master's in Data Science, Computer Science, or related; knowledge of ML algorithmsBachelor's in Statistics, Mathematics, or related; proficiency in data visualization and SQL
Work EnvironmentDeveloping ML models, programming in Python/R, deploying algorithmsData cleaning, reporting, creating dashboards, analyzing datasets
Industry UsageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Full Time Data Scientist Machine Learning roles focus on building and deploying machine learning models, requiring advanced programming and statistical skills. Data Analysts primarily interpret data, generate reports, and support decision-making with less emphasis on ML techniques. Both roles are vital but differ in technical depth and responsibilities.

What cities are hiring for Full Time Data Scientist Machine Learning jobs? Cities with the most Full Time 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 Full Time Data Scientist Machine Learning jobs? States with the most job openings for Full Time Data Scientist Machine Learning jobs include:
Associate Director, Principal Data Scientist - Applied AI & Machine Learning Engineering

Associate Director, Principal Data Scientist - Applied AI & Machine Learning Engineering

The Depository Trust Clearing

Jersey City, NJ • Hybrid

$64K - $65K/yr

Full-time

Medical, Life, Retirement, PTO

Posted 7 days ago


Job description

Are you ready to make an impact at DTCC?  

Do you want to work on innovative projects, collaborate with a dynamic and supportive team, and receive investment in your professional development? At DTCC, we are at the forefront of innovation in the financial markets.  We're committed to helping our employees grow and succeed. We believe that you have the skills and drive to make a real impact.  We foster a thriving internal community and are committed to creating a workplace that looks like the world that we serve.

Pay and Benefits:

  • Competitive compensation, including base pay and annual incentive
  • Comprehensive health and life insurance and well-being benefits
  • Pension 
  • Paid Time Off and Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being.
  • DTCC offers a flexible/hybrid model of 3 days onsite and 2 days remote (onsite Tuesdays, Wednesdays and a third day unique to each team or employee). 

The impact you will have in this role:

Being a member of the Technology Research and Innovation team, you will help advance DTCC's ability to explore, validate, and productize emerging data science, machine learning, AI, and GenAI capabilities that create measurable value across the organization. This role sits at the intersection of research, applied machine learning, data engineering, and product development, with a strong focus on moving ideas beyond experimentation into scalable, production-ready solutions. The Principal Data Scientist / Machine Learning Engineer will design and deliver practical, production-oriented solutions in a financial services environment where governance, risk, controls, reliability, and business adoption are critical. This is not a purely theoretical research role; the ideal candidate will have demonstrated experience taking data science, machine learning, AI, or GenAI concepts from ideation through prototype, validation, productization, and deployment while partnering closely with product owners, technology teams, business stakeholders, and innovation leaders to rapidly develop solutions, test feasibility, define a path to production, and align technical work to real business outcomes.

Your Primary Responsibilities:

  • Champion Python-Centric Data Engineering: Spearhead the adoption and optimization of Python for data engineering tasks, including data ingestion, transformation, and advanced analytics. Guide team in leveraging Python libraries and frameworks to build scalable, maintainable solutions
  • Architect Data Pipeline Solutions: Strategically design and implement enterprise-grade data pipelines for optimal data processing thru python and Snowflake. Establish standards for data quality, security, and integrity, and ensure seamless integration of disparate data sources and formats
  • Strategic Cross-Functional Collaboration: Partner with technology teams to identify opportunities for leveraging data and analytics. Translate business requirements into technical solutions and ensure that insights are actionable and aligned with organizational objectives
  • Lead Advanced Machine Learning Initiatives: Direct the design, development, and deployment of robust machine learning models using Python, guiding teams in data preprocessing, feature engineering, model optimization, and evaluation. Oversee the application of advanced techniques, including deep learning, regression, classification, and clustering, to solve high-impact business problems
  • Demonstrate accountability by taking ownership of solution ideation, development, and execution, including coordinating efforts with internal and external teams/stakeholders to present the results (reports and presentations) in a clear and concise manner
  • Technical Leadership and Mentorship: Provide guidance and technical leadership to junior engineers, create high-performance and reusable approaches to solve challenging problems, and cultivate a culture of excellence, continuous learning, and innovation
  • Risk Management and Compliance: Integrate risk and control processes into all data engineering activities, proactively monitor for potential issues, and escalate risks as appropriate to ensure compliance with organizational standards

**NOTE:  The Primary Responsibilities of this role are not limited to the details above. **

Qualifications:

  • Minimum 8 years of related experience
  • Bachelor's degree (preferred) or equivalent experience

Talents Needed for Success:

  • Extensive experience in data engineering and machine learning model development using Python
  • Proven expertise in architecting data pipelines with Python and Snowflake
  • Strong leadership and mentorship skills, with experience managing and developing technical teams
  • Excellent communication and collaboration abilities
  • Sound understanding of data governance and risk management
  • Experience in Financial industry is preferred
  • Experience in Data Visualization tools is a plus

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

With over 50 years of experience, DTCC is the premier post-trade market infrastructure for the global financial services industry. From 20 locations around the world, DTCC, through its subsidiaries, automates, centralizes, and standardizes the processing of financial transactions, mitigating risk, increasing transparency, enhancing performance and driving efficiency for thousands of broker/dealers, custodian banks and asset managers. Industry owned and governed, the firm innovates purposefully, simplifying the complexities of clearing, settlement, asset servicing, transaction processing, trade reporting and data services across asset classes, bringing enhanced resilience and soundness to existing financial markets while advancing the digital asset ecosystem. In 2024, DTCC's subsidiaries processed securities transactions valued at U.S. $3.7 quadrillion and its depository subsidiary provided custody and asset servicing for securities issues from over 150 countries and territories valued at U.S. $99 trillion. DTCC's Global Trade Repository service, through locally registered, licensed, or approved trade repositories, processes more than 25 billion messages annually. To learn more, please visit us at www.dtcc.com or connect with us on LinkedIn, X, YouTube, Facebook and Instagram.

DTCC proudly supports Flexible Work Arrangements favoring openness and gives people freedom to do their jobs well, by encouraging diverse opinions and emphasizing teamwork.  When you join our team, you'll have an opportunity to make meaningful contributions at a company that is recognized as a thought leader in both the financial services and technology industries. A DTCC career is more than a good way to earn a living. It's the chance to make a difference at a company that's truly one of a kind.

Learn more about Clearance and Settlement by clicking here.

Serves as a dedicated technology resource for advancing DTCC's business opportunities and providing industry thought leadership for leveraging new technology. The goal of this new department is to partner internally with IT, our business and regulatory divisions and externally with clients, regulators, and fintech vendors, to help build new platforms and business models to advance DTCC's mission to support the financial markets.