About the job We are looking for a Chief Machine Learning Scientist who will support our product, sales, leadership, and marketing teams with insights gained from analyzing company data. The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms, and creating/running simulations. They must have a proven ability to drive business results with their data-based insights. They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Responsibilities:
•Guide a team of developers and data scientists and provide leadership to streamline breakthroughs related to the correct prediction of medical diagnoses.
•Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
•Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques, and business strategies.
•Develop company A/B testing framework where applicable.
•Assess the effectiveness and accuracy of new data sources and data gathering techniques.
•Develop custom data models and algorithms to apply to data sets.
•Use predictive modeling to increase and optimize customer experiences, revenue generation, and other business outcomes.
•Coordinate with different functional teams to implement models and monitor outcomes.
•Develop processes and tools to monitor and analyze model performance and data accuracy.
Requirements:
General:
•We’re looking for someone with 5-7 years of experience manipulating data sets and building statistical models, who has a Master’s or PHD in Statistics, Mathematics, Computer Science, or another quantitative field.
•MLOps - A thorough understanding of how to deploy scalable models to the enterprise and manage responsible version control as models are improved.
•Strong problem-solving skills with an emphasis on product development.
•Excellent written and verbal communication skills for coordinating across teams.
ML / Statistics:
•Deep knowledge of a variety of machine learning and statistical techniques (clustering, decision tree learning, regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.) and their real-world advantages/drawbacks.
•Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Decision Trees, text mining, social network analysis, regression, properties of distributions, statistical tests, and proper usage, etc.
•Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, Matplotlib, Plotly, ggplot, etc.
CS:
•Experience using statistical computer languages (R, Python, etc.) to manipulate data and draw insights from large data sets. •Strong Python Coding knowledge and experience (Gensim, spaCy, NLTK, Numpy, Scikit-learn, TensorFlow, Keras, PyTorch, matplotlib)
•Experience working with and creating data architectures.
•Experience querying databases via SQL. •Experience using web services: (e.g., experience with AWS Sagemaker, Elastic Inference, EC2, familiarity with AWS lambda functions, AWS SQS) •Experience with distributed data/computing tools: ElasticSearch, etc.