Responsibilities:
• Consult with internal and external stakeholders to determine how best to apply descriptive analysis and/or statistical learning to support business objectives across 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:
• Masters or Bachelors 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.)

Top 3-5 Requirements:

• Strong knowledge of data science and machine learning
• Tools used for data science machine learning (RPython, Tytorch, Tensor Flow)
• Exposure to End to End Mahcine learning projects
• Good communications and interpersonal skills