Leverage mathematical, statistical, and machine learning methods to inform strategy and decision-making

Employ tools and frameworks like TensorFlow or PyTorch to develop predictive and prescriptive models

Identify and validate data from internal and external sources to uncover meaningful patterns

Evolve hypotheses, analyze data in analytics sandboxes, and iteratively improve models

Use Python libraries such as Pandas, NumPy, and Scikit-learn for data manipulation and analysis

Collaborate with teams to ensure proper data sourcing, cleaning, and preparation for analysis

Optimize and manage relational datasets using SQL while adhering to governance standards

Present insights through visualizations created in Matplotlib, Seaborn, or Plotly

Align organizational data strategies with industry best practices in data management

Deploy machine learning models to production environments via cloud solutions such as AWS SageMaker and EC2

Work with engineers and stakeholders to create scalable solutions for data-heavy workloads

At least 7 years of experience in data science, machine learning, or related domains

Proficiency in Python and libraries like Pandas, NumPy, and Scikit-learn

Knowledge of machine learning frameworks including TensorFlow or PyTorch

Skills in cloud-based tools like AWS, including S3 and SageMaker

Background in big data technologies such as Spark and experience with SQL for relational databases

Familiarity with version control systems such as Git

Understanding of NLP techniques and sentiment analysis systems

Competency in applying data visualization principles with Matplotlib, Seaborn, or Plotly

Background in data governance to ensure compliance and accuracy

Knowledge of Docker for managing containerization and deployments

Background in Snowflake for data warehousing and advanced analytics

Familiarity with tools like Bitbucket, JIRA, and Confluence within the Atlassian suite

Understanding of simulation techniques and scenario-based event processing