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Data Scientist Machine Learning Engineer Jobs (NOW HIRING)

Azumo is currently looking for a highly motivated Data Scientist / Machine Learning Engineer to develop and enhance our data and analytics infrastructure. The position is FULLY REMOTE , based in ...

If you also have knowledge of data science and software engineering, we'd like to meet you. Your ... Design machine learning systems * Research and implement appropriate ML algorithms and tools

Job Title: Machine Learning Engineer Location: Portland, OR - Onsite (Local only / F2F interview ... learning, data science, or AI engineering Strong programming skills in Python (NumPy, Pandas ...

... in machine learning, data science, or AI engineering • Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow) • Experience with time-series data analysis and ...

... in machine learning, data science, or AI engineering • Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow) • Experience with time-series data analysis and ...

You will work closely with data scientists, engineers, and product managers to design, develop, and deploy machine learning models and solutions that drive business value. Key Responsibilities:

Machine Learning Engineer

$117K - $140K/yr

Machine Learning Engineer Apex Systems has an opening for a Remote Machine Learning Engineer ... The Data Science and Analytics COE is responsible for leading the creation and development of the ...

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Data Scientist Machine Learning Engineer information

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$46K

$165K

$243.5K

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

As of Jun 25, 2026, the average yearly pay for data scientist machine learning engineer in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

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

AspectData Scientist Machine Learning EngineerData Analyst
Required CredentialsDegree in CS, Data Science, or related; experience with ML frameworksDegree in Statistics, Math, or related; proficiency in data visualization tools
Work EnvironmentDevelops ML models, algorithms, and scalable solutionsAnalyzes data, creates reports, and visualizations
Industry UsageTech, finance, healthcare, and more; focus on predictive modelingBusiness, marketing, finance; focus on reporting and insights

While Data Scientists Machine Learning Engineers focus on building and deploying machine learning models, Data Analysts primarily interpret data through reports and visualizations. Both roles require strong analytical skills, but Data Scientists Machine Learning Engineers typically have more technical expertise in algorithms and coding, making them more involved in model development and deployment.

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

To thrive as a Data Scientist Machine Learning Engineer, a strong background in statistics, programming (Python, R), and machine learning algorithms is essential, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and experience with big data platforms (Spark, Hadoop) and cloud services (AWS, Azure) are commonly required. Strong problem-solving abilities, communication skills, and a collaborative mindset help professionals translate complex data insights into actionable business solutions. These skills are crucial for effectively designing, deploying, and explaining machine learning models that drive innovation and informed decision-making.

What engineers make $500,000?

Data Scientist and Machine Learning Engineer roles can reach $500,000 annually, especially with extensive experience, advanced skills in programming, statistical analysis, and familiarity with tools like Python, R, and cloud platforms. Compensation often includes base salary, bonuses, and stock options, particularly in high-demand industries or senior positions.

What is a Data Scientist Machine Learning Engineer?

A Data Scientist Machine Learning Engineer is a professional who combines expertise in data analysis, statistical modeling, and software engineering to design, build, and deploy machine learning models. They work with large datasets to extract insights and solve complex problems by developing algorithms and predictive models. In addition to building models, they are responsible for ensuring models are scalable, robust, and integrated into production systems, often collaborating with data engineers and business stakeholders.

How do Data Scientist Machine Learning Engineers typically collaborate with other departments within an organization?

Data Scientist Machine Learning Engineers often work closely with cross-functional teams such as software engineers, product managers, and domain experts. They collaborate to understand business requirements, gather and preprocess data, and integrate machine learning models into production systems. Regular communication is essential to ensure that developed solutions align with organizational goals and are scalable. This collaborative environment not only helps in building robust models but also enhances the engineer’s understanding of real-world business challenges.

Can a data scientist work as a machine learning engineer?

A data scientist can transition to a machine learning engineer role since both involve working with data and algorithms; however, machine learning engineers typically require stronger software engineering skills, experience with deployment, and knowledge of tools like cloud platforms and APIs. Many professionals develop these skills through additional training or certifications to move between these roles.

Is 40 too late for data science?

Data scientists and machine learning engineers can successfully enter the field at age 40 or older, as skills in programming, statistics, and domain knowledge are more important than age. Many professionals transition into data science later in their careers by acquiring relevant certifications, such as those in Python, R, or machine learning, and building a strong portfolio. Age is generally not a barrier if you are committed to continuous learning and adapting to evolving tools and techniques.

Which 5 jobs will survive AI?

Data Scientist and Machine Learning Engineer roles are expected to persist as they involve complex problem-solving, domain expertise, and developing new algorithms that AI cannot fully replicate. Jobs requiring creativity, emotional intelligence, and strategic decision-making, such as healthcare professionals, educators, and specialized technical roles, are also likely to endure. Continuous learning and adapting to new tools like AI frameworks will be essential for these careers.
More about Data Scientist Machine Learning Engineer jobs

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 19 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.