Data Engineer-Machine Learning
IBM
New York, United States4d agovia LinkedIn
This job at a glance
- Who can apply
- New grads, no experience needed
- Must know
- StatisticsStakeholder managementData analysisBudgetingPyTorchTensorFlow
- Nice to have
- Machine learningData structures & algorithms
- Work
- On-site in New York
- Pay
- Not stated by the employer.
- Skill demand
- Stakeholder management37%Statistics30%Data analysis16%Budgeting2%of 100 similar entry-level jobs posted in the US this week
- Listed
- Oct 27, 2026 · still open
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About the Role
As a Data Engineer with expertise in Machine Learning, you will apply Machine Learning concepts and techniques to address business challenges. You will leverage your skills to drive informed decision-making in the organization.
Key Responsibilities
- Develop Machine Learning Solutions: Design and implement Machine Learning models to solve complex business problems, selecting relevant features and algorithms to achieve desired outcomes.
- Evaluate Algorithm Performance: Assess the effectiveness of Machine Learning algorithms using relevant metrics, identifying areas for improvement and optimizing model performance.
- Interpret Statistical Data: Analyze and interpret complex statistical data to inform business decisions, communicating insights and recommendations to stakeholders.
- Communicate Results: Clearly articulate the results of Machine Learning initiatives, providing actionable insights and recommendations to drive business outcomes.
- Drive Informed Decision-Making: Collaborate with stakeholders to integrate Machine Learning insights into business decision-making processes, driving informed strategic choices.
Requirements & Tech Stack
- Deep Expertise in Machine Learning: Proven ability to apply Machine Learning concepts and techniques to address complex business challenges, leveraging expertise in algorithm selection, feature engineering, and model optimization.
- Experience with Statistical Data Analysis: Skilled in interpreting and analyzing complex statistical data to inform business decisions, with the ability to identify relevant trends and patterns.
- Algorithm Evaluation and Optimization: Experienced in evaluating the performance of Machine Learning algorithms using relevant metrics, with a strong understanding of optimization techniques to improve model performance.
- Effective Communication of Results: Adept at clearly articulating the results of Machine Learning initiatives, providing actionable insights and recommendations to drive business outcomes.
- Strategic Decision-Making: Proven ability to collaborate with stakeholders to integrate Machine Learning insights into business decision-making processes, driving informed strategic choices.
Qualifications
- Advanced Statistical Modeling: Experience with developing and applying advanced statistical models to drive business insights, leveraging techniques such as regression analysis, time series forecasting, and hypothesis testing.
- Machine Learning Frameworks: Familiarity with popular Machine Learning frameworks such as TensorFlow, PyTorch, or Scikit-Learn, with the ability to design and implement scalable Machine Learning solutions.
- Data Visualization Tools: Exposure to data visualization tools such as Tableau, Power BI, or D3.js, with the ability to effectively communicate complex data insights to stakeholders.