Senior Data Scientist

Key Skills

Python Programming, Scala Programming, C++, Java Programming, Machine learning techniques, Data science techniques, SQL, Apache Hadoop, MapReduce, TensorFlow, PyTorch, R Programming, Statistics, Biostatistics

Job Description

The Enterprise Data Scientist is the key player in driving advanced analytics and maturing AI at AbbVie, supporting multiple use cases across various business technology organizations in AbbVie; covering but not limited to language/image processing, machine learning analytics, deep learning analytics etc. The use cases cover areas in AbbVie research, commercial operations, manufacturing systems, supply chain planning, logistics, infrastructure processes etc.

The successful Data Scientist will translate business needs into analytic questions; conduct data exploration leveraging data from disparate data stores cross Abbvie (including data lakes, traditional relational and non-relational data); develop model specification; design and perform rigorous analyses of operational, customer, and financial data; and translate these analytic findings into leading information for our business partners.

This position would act as an advisor to executive and management level decision makers. This individual would provide ‘end-to-end’ guidance on deployment of machine learning models including data governance and industry best practices with a lens towards agility and efficiency, leveraging a) data science and statistical methods, b) programming languages such as R, python, Spark, SAS, c) libraries such as TensorFlow, Pytorch etc. d) technology capabilities such as containers and orchestration (Docker, Kubernetes) in on-prem, cloud and hybrid infrastructures.

Responsibilities:

• Consult with internal and external stakeholders to determine how best to apply descriptive analysis and/or statistical learning to support business objectives across AbbVie’s use cases.
• Demonstrate a thorough understanding of concepts related to statistical methods, language and image processing and operations research and how to use them for solving real world problems.
• Apply linear models, machine learning algorithms, times series forecasting, and modern optimization methods (i.e. metaheuristics) to understand and/or predict events impacting various business operations.
• Understand the guidelines needed to build credible and efficient simulation models used to inform the decision-making process.
• Collaborate with subject matter experts and data engineers to deploy advanced analytic solutions into the operational environments.
• Adhere to agile project management frameworks and set the direction of data science initiatives.

Qualifications:

• Master's degree or Bachelor's degree in quantitative discipline (e.g. applied math, operation research, computer science, etc.)
• Lifesciences, healthcare analytics background preferred.
• Practical experience with times series forecasting, monte carlo analysis, spatial analysis, and/or machine learning (random forest, neural nets, SVM, etc.)
• Familiarity and use with public datasets such as Clinicaltrials.gov, imagenet, COCO etc.
• Familiarity with machine learning solution offerings/operationalize from cloud providers such as AWS (ex. Sagemaker), Azure, GCP.
• Familiarity with the concepts of container-based machine learning models, automation, and operations.
• Familiarity with language models (SpaCy, NLTK, Stanford NLP) and using them to operationalize and enhance chatbot user experience.
• Familiarity with navigating in both a relational (Teradata-based) and non-relational (Hadoop) environment. SQL skillset is strongly desired. Knowledge of Java/Scala/Apache Spark is a bonus.
• Proficiency in R/Python; familiarity with libraries such as Tensorflow, café, Pytorch etc.
• Practiced in exploratory data analysis (EDA) and manipulating large data sets.
• Capable of accessing external data sources through various APIs (e.g. google distance matrix, quandl financial data, etc.)

Significant Work Activities

Continuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day)