Data Scientist

We are seeking a highly skilled and experienced Senior Data Scientist to join our dynamic team. The ideal candidate will play a crucial role in driving data-driven decision-making processes, with a focus on marketing mix modeling, database management, and advanced analytics. The candidate should have expertise in data modeling, SQL, Python, R, data warehousing, big data technologies, data pipeline construction, data quality, cleansing, version control, cloud services, machine learning, feature engineering, deep learning, and natural language processing.

If you are passionate about leveraging data to drive business decisions and thrive in a collaborative, fast-paced environment, we encourage you to apply and be part of our innovative team.

Requirements

Roles & Responsibilities

Qualifications:

• Proven experience in marketing mix modeling and database management.
• Strong proficiency in SQL, Python, R, and other relevant programming languages.
• Experience with big data technologies and data warehousing.
• Demonstrated expertise in constructing and optimizing data pipelines.
• Knowledge of cloud services and their application in data science.
• Familiarity with version control systems.
• Solid understanding of machine learning, deep learning, and natural language processing.
• Excellent communication skills with the ability to translate complex findings into actionable insights.

Data Science Skills:

• Marketing Mix Modeling - Develop and implement advanced statistical models to analyze and optimize marketing performance and collaborate with marketing teams to identify key performance indicators and establish metrics for measuring marketing effectiveness.
• Database Management - Design and maintain databases for efficient data storage and retrieval while ensuring data integrity, security, and availability.
• Data Modeling - Create robust data models to support business requirements and analytical needs by working closely with cross-functional teams to understand data needs and design appropriate models.
• Data Pipeline Construction - Develop and maintain end-to-end data pipelines for streamlined data processing and analysis.
• Data Quality and Cleansing - Establish and enforce data quality standards and implement processes for data cleansing and validation.
• Machine Learning and Deep Learning - Apply machine learning and deep learning techniques to solve complex business problems.

Tools and Technologies

• Utilize language such as SQL, Python, and R for data analysis, manipulation, and visualization
• Leverage cloud platforms (e.g., AWS, Azure, Google Cloud/BQ, Databricks) for scalable and efficient data processing and storage.
• Implement natural language processing techniques for text data analysis and understanding.

Nice To Haves

• Implement solutions using big data technologies such as Hadoop, Spark, and others to handle large-scale datasets.
• Engineer features from raw data to improve model performance.
• Stay abreast of the latest advancements in the field and apply them to enhance predictive modeling.
• Familiarity with analytics platforms such as Adobe Analytics or Google Analytics
• Ability to understand analytics and web based language and applications.

Benefits

• Paid Time Off
• Summer Fridays
• Health Insurance
• Profit Sharing Program
• Latest MacBook Pro
• Matching 401K
• Professional Development
• Remote work weeks
• Hybrid work schedule