Data Scientist Senior/Lead

at  Salesforce

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Job Category
Data
Job Details

QUALIFICATIONS

• Bachelor’s or Master’s degree in a technical field.
• 8-10+ years experience in data science and data analytics.
• Expertise with Web analytics platforms, metrics and data models required. Google Analytics preferred. Certification is a plus.
• Strong understanding of SQL and data modeling.
• Proven understanding of common machine learning methodologies such as regression, classification, clustering, and forecasting.
• 3+ years of experience in training, evaluating, testing, and deploying machine learning models.
• Experience with Snowflake or other data warehousing platforms such as Google BigQuery or Amazon Redshift.
• Proficiency in programming languages such as Python or R.
• Proficiency in data visualization and reporting tools such as Tableau.Proven experience in analyzing sophisticated, ambiguous problems and presenting the results to both technical and non-technical audiences.

PREFERRED QUALIFICATIONS

• Understanding of B2B (business-to-business) sales process and datasets.
• Experience in lead technical roles.
• Salesforce product knowledge.
• Experience with dbt or other ETL platforms.
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SALESFORCE IS SEEKING A HIGHLY SKILLED SENIOR DATA SCIENTIST WHO WILL PLAY A PIVOTAL ROLE IN DRIVING DATA-DRIVEN DECISIONS AND STRATEGIES RELATED TO PAID MEDIA EFFORTS AND WEB DIGITAL CHANNELS. THE RIGHT CANDIDATE WILL HAVE DEEP KNOWLEDGE OF STATISTICAL AND MACHINE LEARNING TECHNIQUES TO ANALYZE DIGITAL MARKETING PERFORMANCE ACROSS DIGITAL CHANNELS (PAID MEDIA, WEB, EMAIL), DEVELOP PREDICTIVE MODELS AND OPTIMIZE MARKETING PROGRAMS. CLOSE COLLABORATION WILL BE NEEDED WITH MARKETING AND ANALYTICS TEAMS TO PROVIDE DATA-DRIVEN RECOMMENDATIONS, AUTOMATE PROCESSES, AND ENSURE DATA ACCURACY. THIS ROLE WILL SUPPORT STRATEGIC DECISION MAKING SOUGHT AT IMPROVING THE IMPACT AND EFFICIENCY OF MARKETING PROGRAMS AT SALESFORCE.THIS ROLE RESIDES WITHIN THE MARKETING DECISION SCIENCE DEPARTMENT.RESPONSIBILITIES

• Modeling & Analytics:
• Develop, validate, and implement predictive models and algorithms to optimize channel performance and improve campaign performance.
• Use statistical techniques and machine learning algorithms to segment audiences, forecast trends, and evaluate impact.
• Communicate model outputs to both technical and non-technical partners.
• Continuously monitor model performance and update models as necessary.
• Data Collection & Integration:
• Capture and integrate large, sophisticated datasets from multiple sources including internal CRM systems, advertising platforms, website analytics, and third-party vendors.
• Develop and maintain data pipelines to ensure timely and accurate data availability.
• Ensure data integrity and accuracy by performing routine data audits and validations.
• Clean, pre-process, and transform raw data into structured formats suitable for analysis and machine learning models.
• Collaborate with data engineers and business collaborators to produce source-of-truth datasets.
• Data Analysis & Interpretation:
• Analyze datasets to identify trends, patterns, and insights related to paid media performance.
• Develop key performance indicators (KPIs) and metrics to measure the efficiency of paid media campaigns.
• Perform exploratory data analysis (EDA) to uncover insights and advise strategic decisions.
• Establish and carry out a plan to regularly communicate insights to partners.
• Use statistical and machine learning techniques to identify trends, anomalies, and predictive insights.
• Perform ad hoc analysis to support urgent business questions and needs.
• Quality Assurance & Best Practices:
• Collaborate with data engineers to establish and implement data governance and standard methodologies for data management.
• Conduct regular reviews of data processes to identify areas for improvement.
• Stay updated with industry trends and advancements in data science and analytics.
• Dashboard Development:
• Build and coordinate the development of sophisticated dashboards that provide insights into channel performance.
• Collaborate with data visualization experts and analysts to ensure dashboards are user-friendly and accessible.
• Partner Collaboration:
• Collaborate closely with partner Marketing teams to understand campaign objectives.
• Communicate complicated technical concepts to data engineering teams, third-party consultants and contractors, and be able to simplify them for non-technical audiences as needed.
• Collaborate with data engineers and business partners to produce source-of-truth datasets.
• Leadership and Mentorship:This role will be an individual contributor role that will work closely with a team of analysts, engineers, consultants, technical contractors, product and marketing teams. As the work and remit grows, there is a future potential to build out a team under this leader.