paid / Indianapolis, IN or Charlotte, NC / full-time
The CLA Data Science team within Data Analytics is looking for an experienced R programmer who knows about package development in the R language, version control using git and remote repositories such as github, who is familiar with the tidyverse, including tidymodels, and shiny app development. The Data Science team has a complete R-centric technology stack, including but not limited to: VMs with an updated version of R/RStudio, RStudio Workbench (formerly RStudio Server Pro), RStudio package manager, and RStudio Connect (RSC) for deploying and managing our content. We serve shiny apps and other dynamic or scheduled content to internal stakeholders and our external clients. We are growing and want to find a candidate with a solid background who wants to continue to invest in themselves and grow with us! The team is very supportive of R but is also growing Python capabilities, so both opportunities will exist in the long term. Your primary duties in this role will include helping improve processes, build and lead / oversee machine learning and other quantitative analysis, build and lead shiny app development, and to contribute to our growth minded culture to always be learning!
Data Scientist Senior
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CLA, one of the nation’s largest public accounting and professional services firm is currently seeking a Data Scientist Senior to join our growing Digital Transformation team in our Indianapolis, IN or Charlotte, NC office.
The Data Scientist Senior 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 an Associate, the Senior will develop more autonomy to develop solutions and may lead others, take on administrative tasks, perform support roles, and get involved in new business development.
As a Data Scientist Senior, you will:
- Participate and take ownership in 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 and data gathering techniques.
- Work under the guidance of a variety of Data Analytics team members, gain exposure to developing 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.
- Work individually as well as in collaboration with others.
- Interact with others will primarily be virtual with leadership and colleagues from other offices.
- 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.
Your experience includes:
- 2+ years experience in data analytics, statistics, data science, financial consulting, computer science or related field.
- Education: Bachelor’s degree in a field of Statistics, Computer Science, Economics, Analytics, or Data Science (e.g., Informatics, Data Science, Computer Science, Economics, Analytics or Health Data Science). Master’s degree preferred.
- 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.
- Create exploratory and descriptive analyses using Jupyter, RMarkdown, or similar technology.