Montvale, New Jersey
New York, New York
New York, New York
Posted: 06-Aug-24
Location: New York, NY
Salary: Open
Categories:
Internal Number: 150988
The NYU McSilver Institute for Poverty Policy and Research is committed to creating new knowledge about the root causes of poverty, developing evidence-based interventions to address its consequences, and rapidly translating research findings into action through policy and best practices.
We are seeking to recruit a Senior Research Data Scientist for the Institute and with a special focus on developing applications and solutions to support our Artificial Intelligence (AI) Hub. The AI Hub at McSilver has been established to investigate how artificial intelligence-driven systems can be used to equitably address poverty and challenges relating to race and public health, and to provide thought leadership on the implications. The AI Hub will address a dearth of information about how AI can impact the lives of people in marginalized communities. Among the hub?s initial areas of focus will be building on the institute?s work to answer whether AI can be used to better predict suicide rates and behaviors by race, geography, income and other demographic variables, with other innovative public health research and interventions to follow.
POSITION SUMMARY:
Reporting to the Assistant Director for Research and working in collaboration with the Director for Behavioral Health Research and the Executive Director, the Senior Research Data Scientist will provide expertise, leadership, and strategic planning to drive the growth and expansion of research and evaluation projects within the AI data analytics team. This role involves collaborating with faculty and students on cutting-edge research projects and taking responsibility for pursuing, developing, and expanding research-oriented activities with strategic partners and funders. The successful candidate will be instrumental in leveraging AI to address critical public health and social issues, particularly those affecting marginalized communities.
PRIMARY RESPONSIBILITIES:
Public Health Research and Data Science:
Perform rigorous data science analyses to provide insights and advancements in public health research and policy, focusing on factors such as suicide risk by race, ethnicity, and other demographic variables.
Access, validate, and analyze information from multiple data sources by developing, validating, and linking various data sets, including high-risk, health, behavioral, and public data sets.
Aggregate and analyze population health data, research data, survey data, and other sources to prepare for AI/ML analysis and algorithmic development.
Build and automate data collection, ingestion, and processing tools for AI/ML studies.
Equitable and Ethical AI Integration:
Lead the documentation, planning, design, building, implementation, and maintenance of data management systems (e.g., data lakes) incorporating fair and ethical AI/ML approaches.
Research, identify, and apply standardized benchmarks, thresholds, and metrics as appropriate to ensure ethical AI integration.
Thought Leadership:
Conduct literature reviews independently and write reports and presentations to support publications, grant proposals, and the advancement of research projects.
Provide programming and subject matter expertise to refine the development of new approaches and identify and validate relevant data sets.
Offer advanced statistical support, including recommendations for data and predictive models, data visualization, validation of existing and proposed models and techniques, and development of new data and predictive models.
Create new experimental frameworks to collect, analyze, and categorize data.
SECONDARY RESPONSIBILITIES:
Interdisciplinary Collaboration:
Collaborate with the software development team, prepare research tools and statistical models for deployment on scalable applications and tools, including dashboards.
Collaborate with data sets and data science sources within NYU, government agencies, and other research teams outside of NYU to link program data with other health and personnel data to support AI/ML research.
Workflow Management:
Develop and create documentation regarding the collection, processing, and governance of data sets and methodologies for use in AI Hub research publications.
Oversee the data use agreement compliance process and data governance practices as required by regulatory, privacy, and security policies.
Provide a quality control program to ensure high-level quality of results and maintain a repeatable and transparent workflow.
QUALIFICATIONS:
Minimum Qualifications: To qualify you must have a MS degree and 4-5 years of experience or equivalent combination of education and experience.
Educational Background:
Ph.D. in Data Science, Statistics, Mathematics, Computer Science, Healthcare, Social Work, Sociology, or a related quantitative field. An equivalent combination of education and experience will also be considered.
Research Experience:
Demonstrated ability to develop original statistics and data science research, evidenced by a strong publication record in top-tier journals and conferences or by submitted/accepted papers in prestigious venues.
Demonstrated experience working in an industrial setting with an interdisciplinary team of researchers and developers, or experience which demonstrates open-source development outside of academic papers and venues.
Statistics/AI/ML Experience:
Minimum of 7 years of experience in developing and implementing advanced statistical or data analytical models.
Demonstrated experience conducting data analysis and research using high-risk or restricted data.
Significant experience working with and managing data science workflows involving large data sets.
Proficiency in developing and tuning AI/ML models to ensure accuracy and optimal performance.
In-depth knowledge of statistical analysis techniques and predictive modeling methodologies.
Experience in applying advanced statistical methods and algorithms to real-world data problems.
Analytical and Problem-Solving Skills:
Ability to independently and critically examine, evaluate, and solve complex problems.
Proven ability to synthesize disparate data from multiple sources into accurate and useful analytic products.
Capability to develop and define methods for data collection, processing, tuning, and ensuring data provenance and traceability.
Project Management and Delivery:
Ability to deliver products on time, on schedule, and within budget.
Proven ability to undertake and complete multiple tasks with overlapping deadlines.
Communication and Leadership:
Strong written and verbal communication skills, with the ability to convey research findings using insightful and accurate data visualizations.
Demonstrated ability to effectively communicate analytical discoveries and recommendations to audiences with limited technical knowledge.
Proven experience leading research projects in data science, with a focus on independent work and collaboration within an AI/ML engineering team.
Proven ability to provide accurate and timely analytical products containing well-reasoned and cogent discussion points, and to offer leadership with substantiated options or courses of action.
Required Technological Skills
Programming Languages:
Hands-on experience with Python for developing secure and maintainable code.
Proficiency in additional programming languages such as R, SQL, and Javascript.
Data Tools and Platforms:
Expert knowledge of data tools used for statistical analysis, computing, and processing large data sets.
Proficiency with cloud platforms such as Google Cloud Platform (GCP) or Amazon Web Services (AWS) for data processing and storage.
Experience with data visualization libraries and tools, such as Matplotlib, Seaborn, Plotly, D3.js or Tableau.
Data Management and Analysis:
Strong background in selecting, evaluating, pre-processing, and managing data for predictive analytics, statistical reporting, and AI/ML analysis.
Proficiency in data wrangling and data cleaning techniques.
Ability to develop and define appropriate data collection, processing, tuning, and explainability methods.
Familiarity with big data technologies and frameworks such as Hadoop, Spark, BigQuery, or Kafka for processing and analyzing large-scale data sets.
Version Control:
Experience with experiment tracking and reporting tools like MLFlow, TensorBoard, or Weights & Biases.
Proficiency in data version control using tools like DVC to ensure reproducibility and effectively manage data and model versions.
Proficiency in using Git and GitHub for version control and collaborative software development.
API Development:
Experience developing APIs for data science deployment to facilitate integration with other applications and systems.
Database Management:
Experience with relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra, Firestore).
Project Management and Delivery:
Proficiency in using project management tools such as JIRA, Confluence, and other relevant platforms to track project progress, manage tasks, and facilitate team collaboration.
Experience with Agile development practices, including Scrum and Kanban methodologies, to ensure efficient project execution and continuous improvement.
Preferred Qualifications
Demonstrated experience working in an industrial setting with an interdisciplinary team of researchers and developers is strongly preferred. A background in applied AI in healthcare/public health and experience with algorithmic auditing for real-world applications in social determinants of health is also strongly preferred.
Salary
This position is full-time and includes a generous benefits package. In compliance with NYC?s Pay Transparency Act, the annual base salary range for this position is $100,000- 115,000. New York University considers factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience, education/training, key skills, internal peer equity, as well as specific grant funding and the terms of the research grant when extending an offer.
Please note:
NYU McSilver is currently operating on a hybrid schedule of 3-days a week in-person.
This is a grant funded position with the opportunity for ongoing renewal reviewed on an annual basis.
This position does not qualify for visa sponsorship.
NYU is an EOE/AA/Minorities/Females/Vet/Disabled/Sexual Orientation/Gender Identity employer.
How to Apply
Interested applicants should apply via NYU?s Interfolio system. Be sure to include a cover letter and resume with your application. Only applicants who apply via Interfolio will be considered for this position. The institute seeks to fill this position immediately.
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