Senior Data Scientist

Senior Data Scientist

Requirements

• Proficiency in SQL and Python for data manipulation, analysis, and model development, with advanced knowledge of databases and data representation
• High degree of knowledge in platforms, such as Google Cloud Platform (GCP), Microsoft Azure, or AWS
• Strong analytical and problem-solving skills, with strategic thinking and business acumen to translate business challenges into data solutions and align them with industry trends and business objectives
• Excellent communication and data visualization skills, with the ability to present complex data insights clearly and effectively to non-technical stakeholders
• Curiosity, continuous learning, and adaptability, with a focus on staying updated with the latest advancements in data science and AI and applying new knowledge to projects
• Exhibits ownership, candor, and conscientiousness in their work
• Demonstrates diligence when analyzing data and building models to ensure accuracy and reliability
• Leadership and mentoring skills, with strong people skills to foster collaboration and effective communication within cross-functional teams
• Proficiency in advanced statistical analysis, including regression analysis, time series analysis, and hypothesis testing
• Project management skills, with the ability to manage multiple projects and deliver results on time

Responsibilities

• Leads complex projects, designing, developing, and deploying advanced machine learning models using diverse data to inform strategic decisions, marketing, and advertising strategies, driving business outcomes
• Provides strategic guidance, translating business challenges into data solutions, managing development and delivery, and supporting planning, resource availability, and stakeholder engagement
• Mentors junior team members, supports their growth, and collaborates on downstream steps, including code refactoring, testing, deployment, monitoring, and integration
• Queries, analyzes, and models diverse datasets, including GA4 data, to develop predictive models and automate dataset generation and insights with support from partner teams
• Develops proficiency and utilizes both Google Cloud Platform (GCP) and Microsoft Azure, leveraging tools such as BigQuery, BigQuery ML, Colab Enterprise, Looker, Vertex AI, Cloud Source Repositories, Azure Machine Learning, Fabric, Azure Data Factory, and Azure Repos. Develops and deploys solutions using SQL and Python
• Communicates project status through agile ceremonies, shares insights with stakeholders, develops structured repositories, and operates in an agile environment, utilizing estimation, adaptability, and continual delivery
• Supports the development of planning and estimates for associated scope, ensuring the availability of relevant resources (e.g., datasets and tools). Engages in stakeholder and change management to support the effective use of solutions
• Automates the generation of datasets and insights by deploying data and model pipelines, with support from partner teams, including Data Engineering
• Revises the dataset construction process through iterative development, incorporating feedback and quality assurance (QA) measures to ensure accurate and reliable results
• Explores and remains curious about new areas in data science and AI fields. Conducts research and develops expertise in these areas as relevant to projects, especially through practical application. Maintains a focus on continual improvement

Preferred Qualifications

• Graduate degree (master’s or PhD) in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field, with relevant expertise in data science and AI approaches
• Experience with event-based data, such as GA4
• Experience with standard steps of model development and deployment processes, including end-to-end development, and demonstrated ability to lead complex projects and provide strategic guidance
• Experience mentoring more junior colleagues and working in diverse industries, subject areas, and team sizes
• Experience applying data science to marketing and advertising problems, with specialization in areas such as natural language processing, computer vision, or deep learning
• Experience with agile data science development and end-to-end model development, from data collection and preprocessing to model deployment and monitoring
• Translating model insights into activation and experiments
• Knowledge of data governance, compliance standards, and data ethics, ensuring compliance with relevant regulations and privacy considerations
• Experience with A/B testing and experimental design