Data Scientist Manager

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Summary

The Data Scientist Manager thrives when constructing complex solutions that integrate data wrangling, visualization, and advanced modeling techniques into a seamless workflow using software development best practices in R, Python, or other scripting languages. They are comfortable working with APIs, web scraping, and SQL/no-SQL databases. It is not uncommon for our Data Scientists to automate business workflows while integrating stochastic/numeric algorithms in the process. Creating and modifying predictive algorithms to uncover and answer business questions makes us tick. Our products are usually delivered as a scheduled deployment, markdown report, dashboard, shiny application, or API. In contrast to a Senior, the Manager will develop more autonomy to develop solutions and will lead others, take on administrative tasks, perform support roles, and get involved in new business development.

Essential Job Functions

The below functions reflect the general details of the essential work activities and accountabilities of this position and are not all inclusive. Other activities may be assigned.

Essential Job Functions
• Service Expertise: Develop service specific knowledge through greater exposure to peers, internal experts, clients, regular self-study, and formal training opportunities. Take on lead roles with client situations to develop and enhance business skills. Retain knowledge gained and performance feedback provided to transfer into future work. Approach all problems and projects with a high level of professionalism, objectivity and an open mind to new ideas and solutions.
• Data Analysis: Plan, lead, and execute external and internal projects involving the collection, analysis, and automated collection of data using a variety of data tools. Together with the Data Analytics team, support the building and implementing of models, algorithms, and simulations supporting solutions for external clients and internal projects. Assess the effectiveness and accuracy of new data sources
• Data Development: Collaborate with others on the Data Analytics team members to develop custom data models and algorithms to apply to data sets. Execute predictive and inferential analytics, machine learning, and artificial intelligence techniques. Use existing processes and tools to monitor and analyze solution performance and accuracy, and communicate findings to team members and end users.
• People Development: Assist in developing and executing a Data Analytics/Data Science learning plan for varying levels of knowledge and experience.
• Leadership: Focus on building knowledge of the business of CLA, business development skills, and emotional intelligence. Balance between working independently with specific direction and collaborating with others. Interaction with others will primarily be virtual with leadership and colleagues from other offices. Take a leadership role in developing people.
• Collaboration: Take on additional roles beyond technical development and client service that may include: serving as primary contact with clients or business leaders on internal projects, take on administrative tasks, perform support roles, and get involved in new business development.

Requirements

Experience

Three to seven years of experience in data analytics, statistics, data science, financial consulting, computer science or related field.

Education
• Bachelor's degree in a field of Business, Accounting, Finance, Economics, Analytics, or Data Science (e.g., Informatics, Data Science, Health Data Science) required. Master's degree or Ph. D preferred.

Technical Competencies

Knowledge of and/or Experience* using Microsoft applications including Excel (2016) and expertise in data science and statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets. Experience* working with and creating data architectures or schemas. Knowledge of a variety of machine learning techniques (regression modeling, clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Understand the domain specific nature of data being collected/analyzed and how data may be utilized to satisfy project objectives
• Identify potential data sources that may be useful to harness for analysis
• Identify disparate data sources to harmonize and deliver integrated solutions
• Develop service and industry specific knowledge through greater exposure to peers, internal experts, clients, regular self-study, and formal training
• Transform/wrangle data using dplyr, pandas, or other packages/languages
• Experience with a variety of machine learning models and dimension reduction techniques including but not limited to: linear/logistic regression and other generalized linear models, tree based methods such as CARTs, random forests, boosting, SVMs, penalized methods such as ridge and LASSO (elastic nets), PCA, t-SNE, clustering methods, and other methods that can be applied to create predictive or inferential/descriptive models
• Ability to code in R, Python, SQL, and other TBD languages
• Employ and/or modify existing statistical methodology to solve data problems
• Engineer or derive new features for increased accuracy of future predictions from a trained ML model
• Comfortable with version control (git) in local and remote setting (GitHub/Azure DevOps) and working as a team developing large software solutions
• Write unit tests to assure reliability and accuracy of software results
• Automate extraction of data from disparate data sources in order to support a data product or solution
• Create exploratory and descriptive analyses using Jupyter, RMarkdown, or similar technology
• Utilize logging and other tracking to monitor production processes and solutions
• Develop train, validation and testing frameworks for building and maintaining ML models

* Practical experience beyond education required

Organizational Interfaces

Collaborate with both business and Information Technology (IT) colleagues on data projects. Takes direction from Directors and Principals. The Data Scientist Manager will report directly to the managing principal of Outsourcing Data Analytics.

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CLA exists to create opportunities for our people, our clients, and our communities. We are a proud equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity/expression, disability status, protected veteran status, national origin, or any other characteristic protected by law.

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