Data Scientist, Marketing

Role Overview

• Apply analytical and technical skills to solve real-world business problems.
• Work closely with stakeholders to collect, clean, analyze, and interpret data.
• Contribute to the development and deployment of ML models and data-driven solutions.
• Assist in the collection, cleaning, and preprocessing of large and complex datasets from various sources.
• Conduct exploratory data analysis (EDA) to identify patterns, trends, and insights in the data.
• Apply statistical techniques and machine learning algorithms to build predictive models.
• Develop and validate machine learning models (Supervised and Unsupervised), and other categories such as Ensemble Methods, and Time-series models.
• Collaborate with other data scientists and team members to define project requirements and objectives.
• Communicate findings and insights effectively through visualizations, reports, and presentations.
• Stay up-to-date with the latest advancements in data science, machine learning, and related technologies.
• Document code, methodologies, and results clearly and concisely.
• Participate in the deployment and monitoring of data science models and solutions.
• Contribute to the development of data-driven products and features.
• Assist in the evaluation of new data sources and technologies.
• Independently plan and execute work across multiple projects, stakeholders, and functional areas.

Requirements

• Bachelor's or Master's degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related discipline.
• 3+ years of relevant experience in data analysis, statistical modeling, or machine learning.
• Strong understanding of statistical concepts, probability theory, and experimental design.
• Familiarity with machine learning algorithms (e.g., regression, classification, clustering, dimensionality reduction).
• Proficiency in at least one programming language commonly used in data science (e.g., Python, R).
• Experience with data manipulation and analysis libraries (e.g., Pandas, NumPy, SciPy in Python; dplyr, tidyr in R).
• Familiarity with data visualization libraries (e.g., Matplotlib, Seaborn, Plotly in Python; ggplot2 in R).
• Basic understanding of database concepts and SQL.
• Strong analytical and problem-solving skills with the ability to work with imperfect data.
• Excellent written and verbal communication skills, with the ability to explain technical concepts to non-technical audiences.
• Ability to work independently and collaboratively within a team environment.
• A strong desire to learn and grow in the field of data science.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP). (preferred)
• Familiarity with big data technologies (e.g., Spark, Hadoop). (preferred)
• Experience with version control systems (e.g., Git). (preferred)
• Knowledge of specific industry domains relevant to the company. (preferred)
• Experience with deploying machine learning models into production. (preferred)

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• Hadoop
• Numpy
• Pandas
• Python
• Spark
• SQL

Benefits

• Start Day 1 for Full-Time Colleagues
• No Waiting Period!
• Health benefits including Medical, Dental, Vision, Disability, and Life Insurance plus much more
• 401(k) retirement plan with company-matching contributions
• Vacation days & sick days
• Company-paid holidays & floating holidays
• Professional and Personal Growth
• Multiple avenues to grow your career
• Training and development programs available
• Tuition Reimbursement benefits (for FT Colleagues)