Note: The job is a remote job and is open to candidates in USA. PerkinElmer is a scientific solutions company serving commercial, government, academic, and healthcare customers. The Data Science Intern will analyze and improve the Asset Intelligence Service by tracing data pipelines, measuring classification accuracy, reconciling duplicate records, analyzing attribute coverage, and documenting repeatable methods.
Responsibilities
- Trace an asset record from raw customer intake through normalization, classification, and enrichment to the form it takes in the platform, and be able to explain each step to someone who has never seen it
- Reproduce the pipeline on a sample set and confirm you get the same answers the production service does
- Write down where the service makes a judgment call, what evidence it uses, and which of those calls are the fragile ones. This document does not exist today and the team needs it
- Build and maintain a gold-standard reference set of correctly classified instruments, which is the thing the service is currently missing and the thing every accuracy claim depends on
- Score match rate and classification accuracy against that set, reported by equipment class rather than as one headline number, so the weak classes are visible instead of averaged away
- Re-score after every change, so improvement is demonstrated rather than asserted
- Cluster near-duplicate records and reconcile them as a group with their alias set, rather than one record at a time. Records that differ only by punctuation or spacing are the case the current one-at-a-time approach cannot resolve
- Work on the text that feeds the classifier, separating technical capability language from application and use-case language. Scraped marketing copy about what an instrument has been used for is a known source of misclassification
- Analyze attribute coverage across the record base, field by field, so the team knows which attributes are populated well enough to build on and which are not before anything gets promised to a customer
- Keep the code and queries in the team's repository (notebooks are fine) in a state someone else can pick up and run
- Write short method notes alongside the code. Much of the value of this role is in the write-up, because the point is that the team can repeat and extend the work after the term ends
- Take on ad hoc data pulls and analysis the team needs at short notice. Everyone on a team this size carries some of that, and it is also the fastest way to see how the platform is actually used
Skills
- Currently enrolled in a bachelor's degree program in data science, statistics, computer science, or a related field
- Working knowledge of Python and SQL
- Available approximately ten hours per week during the academic term
- Coursework or project experience in data cleaning, record matching, deduplication, or classification
- Familiarity with pandas and Jupyter, or the equivalent in R
- Some exposure to AI-assisted text processing, along with the instinct to verify what it returns rather than accept it. Knowing how a plausible wrong answer happens matters more here than knowing how to prompt
- Comfortable working remotely from written direction with limited supervision, and inclined to ask early when something is ambiguous instead of guessing and proceeding
- Clear written communication. The analysis is only worth what the write-up conveys
- Interest in scientific instrumentation or laboratory operations. No prior domain knowledge is assumed and none is required to start
Benefits
- Remote work in New York, United States
Company Overview
Company H1B Sponsorship