A Data Scientist is responsible for collecting and analyzing vast amounts of data in order to solve business-related problems. They’ll clean the data up and transform it into a more usable format, then look for patterns and trends in the data that will help the business. In order to do so a data scientist will need to be familiar with programming languages such as SAS, R, and Python, have a solid grasp of statistics, and collaborate with both IT and business sides of a company.
Data scientists have some of the steepest education requirements of all IT occupations. Most positions require an advanced degree, such as a Master’s or PhD in computer science, math and statistics, economics, engineering, or a related field. However, some companies will accept data scientists with a Bachelor’s of Science degree in the above fields.
Example Job Descriptions
3Q Digital is Silicon Valley’s agency of record, and the way we work reflects our roots: we’re relentless, restless, and constantly striving to innovate and drive growth for our clients.
We offer full marketing services including SEM, SEO, social advertising, display, mobile advertising, analytics, and business strategy. We work with the fastest-growing B2C, B2B, ecommerce, and lead gen clients in the U.S. We’re smart and fast-paced, and we value great client service—which is where you come in.
As a Data Scientist, you will be tasked with partnering with key internal and client stakeholders and will be responsible for the development of scalable machine learning and predictive modeling solutions. The ideal candidate will be passionate about solving real-world business problems using statical modeling, machine learning, and artificial intelligence techniques and will be able to act as subject matter expert and be a partner to both our internal teams as well as our day-to-day clients and their extended teams.
You will need to be good with numbers, love to dig into data to find the story, be detail-oriented, know how to focus on what really matters, and you’ll need to understand the importance of accuracy in the data solutions.
You’ll be responsible for:
- Performing algorithmic modeling for media mix optimization while working with the Decision Sciences and client services team to understand and quantify effects of multi-channel marketing.
- Designing and developing Machine Learning and AI models and algorithms that drive performance and provide insights, from prototyping to production deployment, across key areas of interest (e.g., bidding optimization, messaging optimization, multi-touch attribution, marketing mix)
- Integrating with external data sources and APIs to discover interesting trends
- Designing rich data visualizations to communicate complex ideas to internal and external teams
- Designing and managing data QA and validation using automation and best practices.
- Collaborating directly with teams/individuals across the agency to facilitate the design, research, development, and delivery of data statistics, models and client deliverables
- Taking ownership of the various components of Data Science Life cycle: Data Wrangling, Feature Engineering, Data Visualization (discovery), Model Generation
- Partnering with our BI team to build and optimize data pipelines and feedback loops into the models
- Continuous improvement—seeking out opportunities to further develop our analytical, engineering, statistical, etc. toolkit
You’ll need to have:
- A BS in Computer Science, Applied Statistics or related field (MS or PhD preferred); furthermore, MUST have experience in digital marketing and marketing mix modeling
- Expertise in building and applying statistical/mathematical methods, machine learning/predictive modelling with real-world use cases and experience with tools such as R, SAS, SPSS
- Experience with RDBMS technology and SQL (MS SQL or Snowflake preferred) and knowledge of BI related principles such as ETL, data modeling, & data warehousing
- Post-grad experience as a Data Scientist, preferably 5+ years of experience, including experience designing statistical models
- An ability to communicate complex quantitative analysis in a clear, precise, and actionable manner to both technical and non-technical audiences with a desire to work in a collaborative, intellectually curious environment with the ability to interact across various teams
- An ability to manage multiple projects simultaneously with deadlines and manage changing priorities with minimal supervision and intervention