Data Scientist (E5242C)

Institute of Electrical and Electronics Engineers•Piscataway, NJ

23d•Onsite

About The Position

The Data Scientist position is responsible for developing and sustaining an Advanced Analytics practice leveraging Artificial Intelligence (AI), Machine Learning (ML), and generative AI capabilities within the Enterprise Data Science team. This is a hands-on, individual contributor role in which the incumbent will design, develop, and deploy advanced analytics solutions (including classical ML models, large language model (LLM) integrations, and AI-powered automation) in support of business units across IEEE. The role is expected to develop increasing independence over time, with structured guidance from senior team members during onboarding and mentorship throughout project delivery from development through production rollout. The ideal candidate brings foundational expertise in predictive modeling, natural language processing, and data mining, with hands-on experience using modern data tools and ML frameworks. They will collaborate closely with cross-functional stakeholders (including Product, Marketing, Finance, and Technology teams) to translate business needs into data-driven solutions. Familiarity with MLOps practices, model monitoring, and responsible AI principles is a plus. A curiosity for uncovering insights in complex datasets and a drive to create measurable business impact are essential qualities for success in this role.

Requirements

Bachelor's degree or equivalent experience degree in Computer Applications/ Computer Science/ Computer Engineering/Applied Mathematics/ Statistics/ related fields or equivalent experience
2-4 years Hands-on experience in AI and ML model development, including training, evaluating, and iterating on models using real-world datasets
2-4 years Hands-on experience with Python and SQL for data analysis, manipulation, and model development workflows
2-4 years Demonstrated experience, even at a project or academic level, building or experimenting with Generative AI capabilities such as prompt engineering, RAG pipelines, or fine-tuning
2-4 years Familiarity with measuring and evaluating model performance, including basic understanding of metrics for both classical ML and generative AI outputs.
Foundational understanding of statistical analysis, machine learning, deep learning, and natural language processing, with a working knowledge of both classical ML and modern generative AI techniques.
Hands-on experience building and implementing AI/ML models using structured and unstructured datasets, including data wrangling, feature engineering, and model evaluation
Proficiency in Python for data science and machine learning development; familiarity with SQL for data querying and analysis
Basic understanding of cloud platforms (AWS) and how they support model development, data storage, and pipeline infrastructure
Experience with data mining and analysis methods, creating and running simulations, and using or adapting existing algorithms to solve business problems
Familiarity with the generative AI landscape, including large language models and common use cases such as text classification, summarization, and semantic search
Strong data visualization skills with the ability to communicate complex findings clearly to both technical and non-technical audiences
Proven analytical thinking and structured problem-solving skills, with the ability to drive projects from conception through completion with increasing independence and senior guidance as needed
Ability to evaluate software tools, frameworks, and emerging technologies and provide reasoned recommendations
Excellent written and verbal communication skills, with the ability to translate technical concepts into business terms and build consensus across stakeholders
Ability to develop and maintain strong working relationships across organizational levels, including product, engineering, and business teams
Good documentation and coding practices that support maintainable, reproducible, and scalable development
Individuals currently serving on an IEEE board or committee are not eligible to apply.
This position is not budgeted for employer-sponsored immigration support, this includes all persons in F (both CPT and OPT), J, H, L, or O status.

Nice To Haves

Advance degree a plus
Less than 2 years Experience developing data visualizations and dashboards using tools such as Tableau, Power BI, or similar platforms to communicate insights to stakeholders
Less than 2 years Experience building data integration, data mining, or ETL/ELT pipelines, with familiarity in modern cloud-based data platforms
Familiarity with MLOps practices, model monitoring, and responsible AI principles is a plus.

Responsibilities

Design and develop predictive models, AI/ML algorithms, and generative AI solutions for initial prototypes and production deployment across IEEE business units.
Build and maintain data pipelines and data collection processes to support model development, ensuring large, high-quality datasets are available for training and evaluation.
Apply data mining, statistical analysis, and machine learning techniques using structured and unstructured datasets to uncover actionable insights for business stakeholders.
Study, explore, and evaluate new and existing data sources to assess their effectiveness and accuracy for use in AI/ML models; provide clear recommendations based on findings.
Support the development and fine-tuning of language models for tasks such as topic modeling, similarity search, text summarization, sentiment analysis, and named entity recognition.
Participate in cross-functional collaboration with Product, Marketing, Technology, and other stakeholders to define requirements for data science and AI solutions.
Assist in data wrangling, standardization, and assimilation to ensure clean, reliable inputs for modeling and analytics workflows.
Evaluate emerging AI tools, frameworks, and open-source technologies; contribute recommendations for adoption into the team's technology stack.
Collaborate with IT and Engineering teams on implementation, integration, and security considerations for new tools and models.
Assist in delivering end-to-end analytics projects, including proofs of concept (POCs), from problem definition through model launch and stakeholder communication.