Job description
How Will You Make an Impact?
Thermo Fisher Scientific Inc. (NYSE: TMO) is the world leader in serving science enabling our customers to make the world healthier, cleaner and safer. We are at the heart of global response to COVID-19 outbreak and have delivered 1/4 of the COVID-19 testing needs. Our customers includes hospitals and clinical diagnostic labs, pharmaceutical and biotech companies, universities, research institutions and government agencies. You will contribute your part to make a better world.
What Will You Do?
You will help to deliver deep analytical solutions using data science processes (CRISP-DM and Agile), technology, and machine learning algorithms to the microarray group marching towards Industrial 4.0 in Thermo Fisher Scientific. You will apply your knowledge of statistics, machine learning, data structures, programming to recognize patterns, identify opportunities and translate into valuable business solutions.
- Provide support to data science projects including analysis, querying, coding, visualization, modeling, and deployment.
- Be involved with many aspects of model design and evaluation tasks and prove the efficacy of models built using business driven measurements and sound statistical principles.
- Automate predictive processes with monitoring tools, anomaly detection, time series forecasting, and other potential algorithmic solutions that deliver better insights of our manufacturing process and activities that leverage data science technology to drive incremental impact to the business.
- Work with engineers and other stakeholders to ensure the effectiveness of models with rigorous scientific observation and evaluation of data science products.
- Work with cross functional teams to support data collection, visualization, and integration.
- Collaborate closely with other data scientists on developing and improving model performance and conduct other ad hoc analysis as needed
How Will You Get Here?
- Being a undergraduate or post graduate student, major in machine learning, statistics, AI or related to business/data analysis
- Good understanding of statistics and machine learning concepts and willing to learn with a desire to contribute using real world data
- Familiarity with Python or R and SQL
- A plus to be familiar with cloud architecture (AWS preferred), experience with Linux, SageMaker, deep learning, image classification
- Ability to work both independently and as part of a team
Learning Outcome (at least 3 points):
- By end of internship, the student will learn:
- Gain experience of complete life cycle of data science project with business understanding, data ETL, exploratory analysis, modeling, evaluation and deployment.
- Get familiar and utilize tool like Python/R, SQL, PowerBI, AWS Cloud to solve real world business issues
- Apply knowledge learned in the University and acquire skills needed to become hands-on data scientists
- Gain interpersonal skills that promotes personal growth and development