Revenue Platforms Data Engineer

Key Skills

ETL frameworks, Machine learning techniques, Python Programming, SQL, Textract, Transformer

Job Description

Are you passionate about making huge business impacts through data-driven insights and developing game-changing new capabilities? Do you love working collaboratively in an innovative environment? Would you love to make a direct impact on customers’ lives? Come join the TurboTax Buyer Journey analytics team as a Staff Data Scientist focused on web personalization capabilities and analytics. You will be joining a team that is responsible for the end-to-end customer journey from first touch to diving into the tax prep process. As the data science leader for web personalization, you will develop new models and insights to personalize the site experience for potential and existing TurboTax customers, deliver new experimentation methods to better understand incremental impact of personalization, and collaborate with product, marketing, digital activation, and engineering teams to create a personalization center of excellence.

Responsibilities

• Develop machine learning models and other approaches for key attribute identification in order to optimize content and user experience based on behavioral data.
• Lead development of a web personalization strategy, including identification of data sources and metrics and partner with engineering to develop feature engineering solutions for real-time personalization.
• Collaborate with cross-functional teams to design and implement data-driven personalization experiments.
• Develop and implement a structured experimentation framework for testing and validating personalized content and recommendations, including identifying key metrics and KPIs to measure performance and ROI.
• Communicate insights and recommendations to key stakeholders, including senior leadership, around benefit of personalization and recognition.

Qualifications

• 10+ years of experience working with sales analytics, product analytics, web analytics, customer analytics, or other customer experience analytics
• Technical proficiency in SQL, Databrick, A/B testing, Tableau, and Excel
• Experience in Advanced Analytics (e.g. Python, R) and developing and deploying machine learning models preferred.
• Ability to tell stories with data, influence business decisions at a leadership level, and provide solutions to business problems
• Exceptional problem-solving, quantitative thinking, organization, time management, and task prioritization skills
• Outstanding communication skills with both technical and non-technical audiences
• Bachelor’s Degree in Statistics, Mathematics, Data Analytics, Finance, Advanced Analytics, or related field. Master’s Degree preferred; equivalent experience will be considered

Minimum qualifications:

• Bachelor's degree in Business, Finance, a related quantitative field, or equivalent practical experience.
• 3 years of experience with Finance systems, accounting, operations, tax, internal controls, requirements documentation, testing, and validation.
• Experience of SQL/database management.
• Experience of Object-Oriented Programming.

Preferred qualifications:

• Experience in collaborative coding and version control.
• Experience in machine learning, including data preparation, model selection, performance evaluation, and parameter tuning.
• Ability to maintain professional and influential presence with excellent communication and customer service skills.
• Ability to drive operational process improvements.
• Passion for developing and analyzing complex data sets and converting them into actionable business insights.

About the job

In this role your mission is to simplify and centralize the prioritization and deployment of smart, Quote-to-Report (Q2R) digitalization to deliver the highest Return on Investment (ROI) on Finance platforms, for Alphabet. Ultimately, we aim to enable Q2R systems, processes and data fit for Finance that allow every product, anywhere, anytime, the right way, effortlessly.

Responsibilities

• Collaborate with Stakeholders work with finance Subject matter experts to understand report requirements and provide necessary data solutions, access and support.
• Develop and Maintain Data Pipelines design and implement Extract, Transform, and Load (ETL) processes (SQL, Python) to ensure timely and accurate collection and integration of financial data from various sources.
• Manage Data Architecture understands internal infrastructure to select appropriate sources and technologies to manage data, and design data architecture to support financial reporting. Ensure data security and compliance.
• Ensure Data Quality implements data validation and cleansing processes to maintain quality data for accurate revenue reporting.
• Automate manual tasks and design scalable data solutions. Efficiently handle volumes of financial data.