Data Science Fellow
Learning Ally is a leading nonprofit education solutions organization that transforms the lives of struggling learners by delivering proven literacy solutions that help students reach their potential. Our mission is to radically change education, transforming the lives of children by providing reading interventions and solutions to help them succeed academically, build self-confidence, and thrive in school and beyond. In the US, 65% of fourth graders read below proficiency and are 400% more likely to drop-out of high school. For most “at risk” students (students of color, low income, English Language Learners and students with learning disabilities) the problem is even more acute.
Learning Ally seeks to break this cycle. With early student assessment, intervention and accommodation and professional development for educators, Learning Ally can identify and prevent learning issues by working with schools and educators to support new and struggling readers. Our solutions build a strong foundation for independent, engaged learners who are empowered to achieve socially, emotionally, and academically, regardless of background or learning difference.
Founded in 1948 as Recording for the Blind, to help soldiers who lost their sight in combat continue their education with audiobook products, Learning Ally has evolved to help individuals understand the unique ways they learn and match them to the solutions that enable personal achievement. Today, Learning Ally reaches over 1,500,000 students, 200,000 educators and 18,550 schools across the country and continues to expand its reach. With a continued commitment to supporting students who learn differently, Learning Ally has joined researchers and neuroscientists from renowned institutions including UCSF and MIT, to better understand and address learning issues. You will find that our culture is one that is very committed to our mission, innovation, professional growth, and diversity, equity, and inclusion.
About the Data Science Fellow
The Data Science Fellow is a program designed for exceptional candidates who’ve demonstrated interest and passion for data science but may not yet fully possess technical training in data extraction and machine learning techniques typical of a data scientist. This program enables these candidates to develop skills in data extraction, data analysis, and business acumen while assisting in the resolution of data problems related to Learning Ally’s mission of ending the literacy gap in the United States by 2040.
Ultimately, outputs created by the Data Science Fellow enhance our ability to:
- Grow reach and revenue - develop high quality predictive models that better identify well-qualified prospective K-12 school customers. In conjunction with sales leadership, continually refine and update predictive models that drive greater growth among Learning Ally K-12 sales
- Surface insights that deepen engagement among users of Learning Ally solutions and services - create predictive models that examine the behaviors and selections of well-engaged Learning Ally users and use these to create algorithms that drive positive behaviors among other members and which ultimately lead to greater engagement with Learning Ally services
- Inform the development of new data-driven solutions and value proposition - develop statistical models that mine insights among Learning Ally’s community and which can be developed into value-add solutions that enable new solutions and services
- Address strategic business problems - help improve Learning Ally’s ability to solve
complex business problems by bringing together disparate data streams and statistical analysis in order to drive improved decision makers
As a Data Science Fellow, you’re expected to be able to:
- Commit to taking the developmental steps needed in order to enhance your prowess around data extraction, data science analysis, and business acumen
- Work in multi-disciplinary and cross-functional teams to decode business requirements into machine learning based goals and modeling approaches; rapidly iterate model structure and design through parameter tuning, data transformation, and accuracy measurement selection to refine and validate approach
- Operate in a fast-paced and dynamic environment with both virtual and face-to-face interactions; communicating results and insights, and educating others through insightful visualizations, reports, and presentations adapted for both technical and non-technical audiences
- Empower others to be self-sufficient in data by building self-service tools and reports to drive awareness and understanding of key metrics
- Analyze large-scale structured and unstructured data; develop deep-dive analysis and machine learning models to drive member value and customer success.
- Support the design of experiments to test new product ideas or go to market strategies. Convert the results into actionable recommendations.
- Applies analytical rigor and statistical methods to analyze large amounts of data, using advanced statistical techniques such as predictive statistical models, customer profiling, segmentation analysis, survey design and analysis and data mining.
About the Data Science Fellow Curriculum
As a Data Science Fellow, you’ll develop key skills around areas of data extraction, data analysis, communication, and business acumen. Further detail is provided below.
- Skill in data extraction
- Proficiency with SQL: SQL is the primary and foremost necessary concept at Learning Ally. Our data is stored in Relational SQL tables. We expect data scientists to know RDBMS in-depth to access, retrieve, and manipulate the data through SQL. We expect Data scientists to build and write complex queries involving joins, sub-queries and extract data to use it for further analysis.
- Proficiency with organizing data structures - Data scientists should be proficient with all the data produced in different systems across the business and understand how other systems are integrated, able to join data from disparate systems, and able to stitch together different data streams and make meaning. For example: Understand Building Data and connecting features available internally in NS, DMART, MDR, Marketo, and Excite.
- Proficiency with our business systems:
- Data Mart (DMART) and PowerBI: We have Microsoft SQL Server Analysis Services (SSAS) Cubes developed using Data Mart, which gathers data from different business systems. These existing cubes have defined business rules and can be accessed using PowerBI. Data related to ABS involving utilization of schools and users can be accessed via cubes in PowerBI. In addition, Cubes primarily use DAX Query language, and the data scientist should possess knowledge of the same. In addition, data scientists should be able to understand needs from other business units and translate those needs into data measures and work alongside Data Architect to have them produced in DMART.
- NetSuite (NS): Netsuite acts as our Customer Relationship Management (CRM) tool and has all the information of our relationships with customers and is primarily used by the Sales, Ed Success, Customer Success, and Finance departments. Data scientists should be able to understand different components of our CRM, possess knowledge of data available in NS, understand different integrations that exist in NS, build basic queries in NS, work with the NS team, and articulate needs for complex data requirements.
- Marketo: Marketing uses Marketo for prospecting and nurturing leads. Data scientists should be able to query the entire database of Leads in Marketo and be successfully able to retrieve leads based on specific criteria. In addition, work with Marketing to understand the different campaigns in flight that may affect leads.
- Launchpad (Excite): Excite has been developed on a new platform, “Launchpad,” that integrates with Clever. The Data Scientist needs to understand the overall Excite Database Schema, the hierarchy of Districts and Schools, User Information to extract insights on their usage behavior. All of the data is stored in SQL. Given that it is in the early stage of development, the data scientist would be expected to run queries and gather data for analysis continuously.
- Glide: The data scientist must understand glide and build applications as the Data Science team internally uses it to stitch different data streams together and share insights and data with other groups.
- Skill in Analysis :
- Spreadsheets (Google Sheets/ Excel): Data scientists should be proficient when operating with Excel and are expected to analyze data, write formulas to summarize, and present the information. Here are some of the functions to be proficient in - conditional formatting, Ranges, Tables, Text functions, Date & Time functions, Subtotals, lookups, etc.
- Programming in Python - Data scientists should be proficient with Python, especially in Jupyter Notebooks, and should be experienced with two packages, namely: Pandas and Numpy. Once we have extracted data, the Data Scientist would use Python to transform data and visualize and examine other insights.
- Basic Statistics for Data Analysis: Data scientists are required to have a good understanding of certain critical statistical concepts for data analysis which include: Probability distribution functions (PDFs), Mean, Variance, Standard Deviation, Percentiles, Covariance, Correlation.
- Applied Machine Learning:
- Visualization: Data scientists should be familiar with using Matplotlib in Python as it is extensively used for data visualization and offers a lot of graphs and plots. In addition, the data scientist should also be comfortable plotting charts in spreadsheets.
- Machine Learning Algorithms: The data scientists should be comfortable using scikit learn, a machine learning library that provides almost all the machine learning algorithms. Further, the data scientist should be comfortable applying clustering, classification, regression, and dimensionality reduction algorithms and articulate what techniques to use based on the problem set.
- Skill in Communication :
- Synthesize insights into crisp narratives and present to senior leadership and also able to effectively communicate findings and evangelize data-driven business decisions
- Business Acumen/ Background knowledge of our Business:
- Understanding of our revenue model including various revenue streams, the nature of our subscription based business and segmentation within our K-12 Customers
- Understanding the different stages involved in a customer journey: Acquisition, Activation, Adoption and Advocacy
- Understanding different Solutions that we offer, our target audiences and the eligibility criteria for using our products.
Skills and Qualifications
- Bachelor’s degree or higher with evidence of applied statistics and analysis, Master’s Degree preferred
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
- Experience with common data science toolkits, such as R, Python, NumPy, MatLab, etc.
- Experience with data visualisation tools, such as Microsoft PowerBI, Tableau, D3.js, GGplot, etc.
- Proficiency in using query languages such as SQL
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
- Data-oriented personality with ability to communicate findings across diverse field of business stakeholders
- Strong written and oral communication skills
- Ability to work successfully as part of a team
Learning Ally is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status.