ETS’s Research & Development division is seeking a creative and innovative Data Scientist to join the Language Learning, Teaching, and Assessment (LLTA) research laboratory. The Data Scientist will join a team of research scientists, engineers, user research specialists, and product owners in the agile development of market-responsive adaptive and personalized capabilities, prototypes, and product concepts. The Lab will be focused on prototyping capabilities and product concepts that enable interconnected language learning and assessment systems and interactive educator training programs. The Lab also prototypes capabilities to increase language learning and assessment development efficiencies through automation.
The successful Data Scientist candidate will (a) identify valuable data points that together construct a user data footprint and ensure they are captured in solutions (b) conduct research and development of statistical models (c) develop and execute machine learning and deep learning experiments (d) use advanced data visualization techniques to communicate outcomes and insights (e) work collaboratively with Lab teams to use insights from machine learning and deep learning experiments to inform solution development and optimization.
- Develop and build user data footprints within discrete teaching, training, learning, and assessment solutions. Aligned projects may include (1) data architecture development (2) identification of value engagement, usage, progression, and performance data points (3) automation of collection processes.
- Conduct the preprocessing of structured and unstructured data in small to moderate sized data sets. Preprocessing of data may include (1) data cleaning (a) missing data (b) noisy data (2) data transformation (a) normalization (b) attribute selection (c) discretization (d) concept hierarchy generation (3) data reduction (a) aggregation (b) subset selection (c) numerosity and dimensionality reduction.
- Lead the research and development of statistical models. Research and development projects may include (1) exploring data sets to discover trends and patters (2) build predictive models and machine-learning and deep learning algorithms (3) develop new methods for uncovering trends (4) combining models through ensemble modeling.
- Implement data visualization techniques to communicate results and ideas to key decision makers.
- Contribute to the identification of proposed solutions and strategies based on insights gleaned from analyses and model building. Solutions and strategies might include (1) processes for increasing internal efficiencies (2) optimization of existing products, programs, and services (3) development of new products, programs, and services.
- Collaborate closely with engineers and developers to support the interpretation and implementation of data insights.
- Follow all QA, documentation, and code archive processes. Suggest optimizations to that process were appropriate.
Experience and Skills
Education, Certifications, or Special Licenses:
- At the Associate and Data Scientist level a bachelor’s degree in Computer Science, Engineering, Educational Technology or a related field is required; a graduate degree preferred.
- At the Senior and Principal Data Scientist level a master’s degree in Computer Science, Engineering, Educational Technology or a related field is required.
Relevant Years of Experience:
- At the Associate level, at least one-year proven experience in data mining and machine learning. At least one-year proven use of data frameworks and BI tools.
- At the Data Scientist level, at least four years of progressively independent experience in data mining, predictive modeling, and the development of machine-learning algorithms using R, SQL, and Python. At least four years of progressively independent experience using data visualization techniques to communicate insights to decision makers and support the implementation of those insights.
- At the Senior level, at least eight years of progressively independent experience in data mining, predictive modeling, and the development of machine-learning algorithms using R, SQL, and Python. At least eight years of progressively independent experience using data visualization techniques to communicate insights to decision makers and support the implementation of those insights
- At the Principal level, twelve years of progressively independent experience in data mining, predictive analytics, machine learning with educational technology tools. Demonstrated experience communicating methods, models, and processes externally and contributing to the broader field of data science. At least four years at the senior level and evidence of mentoring and developing more junior staff is required.
- Experience using insights from data science to inform product or program development and/or optimization.
- Proficiency in R, SQL, and Python.
- Proficiency in data visualization tools like Tableau
- Proficiency in data frameworks like Hadoop