Ascend Learning; LLC (www.ascendlearning.com) is a leading provider of technology-based educational, curriculum and assessment solutions for high-growth careers in a range of industries, including healthcare, fitness and wellness, and insurance and financial services. We provide outcomes-based solutions aimed at helping people enter, sustain and succeed in their chosen fields. Ascend employs more than 1,000 employees, with headquarters in Burlington, Mass., and offices in Kansas City metro, Phoenix metro, Denver metro, Minneapolis-St. Paul, Walnut Creek, California, Ann Arbor, Michigan, New York, and the U.K
Ascend Learning is hiring a Senior Data Scientist to join our team. Reporting to the VP Data and Analytics, this person is responsible leading a small team of data scientists and providing hands-on guidance for delivering actionable insights and associated value to the businesses. This includes improving learner outcomes, driving efficacy in our products, answering financial questions, optimizing sales/marketing strategies and many more exciting use cases.
Develop a center of excellence around data science best practices to drive consistency on methodologies and technology selection
Strategically influence analytics roadmaps that drive value to Ascend business units and its clients, differentiating the business unit from its competition
Continually ideate and identify ways in which analytics can drive value for Ascend and/or Ascend customers
Review, direct, mentor, inspire the analytical work of more junior data scientists
Education and Experience
BS or Masters in the following areas: Statistics, Data Science, Computer Science, Mathematics or Operations Research; PhD is preferred
A minimum of 5 years’ experience working on data science, machine learning, or artificial intelligence projects in the industry
Skills and Abilities (non-technical)
Intrinsic motivation with a strong desire to make a positive impact on the organization
Passion for combining both creative and logical critical thinking skills to solve complex, loosely defined problems
Robust sense of intellectual curiosity and the tenacity to experiment
Aspirations for professional development and growth opportunities
Excellent verbal, written, and presentation communication skills
Skills and Abilities (technical)
Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, Boosting, Ensembling, Neural Networks, etc.
Strong background in text analysis and NLP
Experience with common data science toolkits, such as Python NumPy, SciPy, Scikit-learn, TensorFlow, Theano, Caffe, R, MatLab, Weka, Spark ML, Azure ML, etc.
Excellent scripting and programming skills in Python and R with focus on clarity, reproducibility, and reusability
Experience with relational (SQL Server, Oracle, MySQL, PostgreSQL) and NoSQL (Elastic Search, MongoDB, HBase, Cassandra)
Experience working with unstructured data (email, documents, chats)
Proficiency in using query languages such as SQL, Hive, Pig, Lucene search
Good applied statistics skills, such as hypothesis testing, distributions, bias/variance trade-offs, statistical testing, regression, as well as linear algebra e.g. PCA
Experience training/tuning machine learning models to be used in production environments for real-time processing
Experience with data visualization tools, such as Tableau, Power BI, D3.js, GGplot, Matplotlib, etc.
Experience with cloud infrastructure is a plus
Exposure to cloud cognitive services such as vision, speech, knowledge, search, and language on Azure, Google Cloud, or AWS is a plus
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