Data Analyst Intern-Pieces

Website Pieces


Healthcare is about decision-making – and the best decisions are those made with complete and comprehensive data. At Pieces, we collect and deliver data from every part of the patient care journey to help healthcare providers and social service organizations keep communities flourishing.

Pieces uses the most cutting-edge technology to help vulnerable populations get the care they need. We use artificial intelligence, natural language processing (NLP), and machine learning to address clinical and social determinants of health. Our tools provide the insights needed to improve patient care beyond the hospital walls.

In plain terms, we’re building technology platforms to make better patient care easy. And we need a smart Data Analyst to help us achieve our mission.

The Data Analyst Intern is responsible for supporting advanced analytics and business intelligence on health artificial intelligence (AI) products to enable knowledge discovery in health care organizations. Our goal is to use these findings to improve patient care quality, outcomes, and clinical decision making. This role directly supports internal business partners by providing practical analytics solutions for emerging product development, delivery, and client engagement requests.


Duties, responsibilities and activities may change or new ones may be assigned at any time with or without notice. Specific responsibilities include but not limited to:

    • Derive business insights from data, answer clinical questions using data science techniques, respond to on-demand requests for general business reports, support data analysis for new product design and development.
    • Implement basic business intelligence techniques to answer business questions, such as performance analysis on models and products.
    • Utilize the data engineering pipelines and other features/tools in data science infrastructure to support end-to-end analytics solutions.
    • Participate in project preparation, exploring the scope, depth, and challenges of new projects, and formulating strategies and action plans.
    • Responsible for data scoping, exploration, identification, extraction, cleaning, applying logic, verifying data elements and constructing the database that is relevant to individual projects, and other data analysis processes.
    • Support efforts in dissecting, designing, analyzing, developing, summarizing, and presenting predictive modeling, machine learning, and other advanced analytics solutions to key stakeholders.
    • Take initiatives to conduct in-depth assessment and selection of the most appropriate methodologies for the project given its complexity, scope, and data structure for overall descriptive statistics, exploratory data analysis, including graphs, schematic summaries, and necessary transformations and confirmatory data analysis, including statistical tests and/or modeling.
    • Responsible for tracking project-specific issues from initiation through release. Independently identifies & troubleshoots problems encountered during the process. Responsible for preparation of data files and statistical reports for project release. Identifies systematic problems with data and software.
    • Stays abreast of the latest developments, advancements, and trends in the field of data science by attending seminars/workshops, reading professional journals, and actively participating in professional organizations. Integrates knowledge gained into current work practices.
    • Maintains knowledge of applicable rules, regulations, policies, laws and guidelines that impact the assigned department. Develops effective internal controls designed to promote adherence with applicable laws, accreditation agency requirements, and federal, state, and private health plans. Seeks advice and guidance as needed to ensure proper understanding.

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Required Skills

    • Hands-on experience with database and data pipeline development.
    • Research and development experience in predictive modeling, data mining, or other advanced analytics is a plus.
    • Experience with Docker and Kubernetes is a plus
    • Experience with Natural Language Processing is a plus.
    • Team-based work and communication tools (Google Doc, Sheet, Atlassian, etc).
    • Ability to self-manage, work independently and meet deadlines.
    • Critical thinking skills.


Required Experience

    • Master’s or higher degree in computer science, data science, statistics, applied math, informatics, business analytics, or a related field.
    • Two years of experience in analysis of healthcare or business related data and information system.


    • Hands-on experience with database and data pipeline development.
    • Research and development experience in predictive modeling, data mining, or other advanced analytics is a plus.
    • Proficient with programming languages including Python and SQL in performing data query, cleaning, manipulation, processing management and analysis is required.
    • Knowledge of informatics, and statistical methods, concepts, practices, procedures and their applications.
    • Proficient with Linux and MacOS operating systems.


  • Paid internship
  • The chance to be part of a rapidly growing startup
  • Solid experience in developing health NLP systems on real-life applications
  • A comprehensive tech setup
  • Healthy snacks
  • Free arcade games (de-stress while trying to knock off the reigning Pac-Man office champion!)
  • The team at Pieces is a driven bunch of individuals working toward a common goal. When you join our team, you’ll find a mission-driven culture built on:
  • Positivity: we work hard, and we are enthusiastic about problem-solving and empathetic to our partners’ causes.
  • Teamwork: we believe in harnessing the strength of the collective, mentorship, and amplifying each other’s strengths.
  • Great communication: clear, concise, empathetic communication is paramount to our success.
For example: I am very interested in the position at [company]. I believe my skills and work experience make me an ideal candidate for this role. I look forward to speaking with you soon about this position.
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