Data Scientist

2 weeks ago

Andover, United States eTeam Full time

Title: Data Scientist

Location: Andover, MA

Duration: 12 Months

Pay Rate: $41.00 on w2

Schedule: Monday - Friday 9am -pm


******W2 Contract ONLY ***** US Citizens & Permanent Resident ONLY******


Interview:

First round: 1:1 with HM via phone or teams

Second round: Panel via teams


HM notes: Preferred degree in Engineering or Physics. Preferred Experience/background with Math and programing skills.


Must have:

- Programming experience in Python, MATLAB, Julia, R, or any object-oriented programming language.

- Machine learning and modeling experience.


Nice to have:

- CFD modeling

- Mechanistic modeling experience


Description:

An Associate Scientist/Senior Associate Scientist position focused on data science and process modeling is available within the Manufacturing Sciences and Technology (MSAT) team at Client.

This position will support a larger team of scientists and engineers across multiple sites focused on Client’s portfolio of late-stage development and commercial biopharmaceuticals and vaccines.

The position will work across the Client’s Global Supply (PGS) organization in conjunction with the Bioprocess R&D organization to develop multivariate data analyses, machine learning models, mechanistic models and simulations for the characterization of bioprocesses and operations at multiple scales.

In this role the candidate will build models for our operations and processes worldwide and conduct computational modeling and simulation studies to enable detailed visualization and understanding of our process portfolio across multiple scales and operating under various conditions.

The candidate will also collaborate with manufacturing operations and Bioprocess R&D in digital twin initiatives as a subject matter expert to ensure modeling projects meets the needs of PGS.

The colleague may additionally be involved in innovation and technology development projects that are at the intersection of state-of-the-art methods in mechanistic modeling, machine learning and artificial intelligence to improve production processes and have the potential to increase process performance and robustness.

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