Data Scientist (Sales and Marketing Analytics)

Job Description

Role Overview: We are looking for an experienced Data Scientist to derive actionable insights from sales and marketing data. This role will focus on leveraging advanced data analytics, statistical modeling, and machine learning to uncover trends, predict outcomes, and guide strategic decisions. The ideal candidate will play a crucial role in bridging sales and marketing efforts, enabling data-driven alignment and performance optimization. Key Responsibilities: 1. Data Analysis and Insight Generation: a. Analyze large datasets from sales and marketing platforms to identify patterns, trends, and opportunities. b. Develop actionable insights to improve lead generation, conversion rates, and campaign performance. 2. Predictive Modeling: a. Build and deploy predictive models to forecast sales performance and marketing campaign outcomes. b. Use machine learning algorithms to segment customers and predict their behavior. 3. KPI Optimization: a. Analyze key performance indicators (KPIs) for sales and marketing teams to identify areas for improvement. b. Provide recommendations to optimize sales pipelines and marketing campaign ROI. 4. Data Integration and Automation: a. Integrate data from multiple sources (e.g., Salesforce, Google Analytics, Facebook Ads) into a unified analytics framework. b. Automate reporting and visualization processes using advanced tools. 5. Collaboration with Teams: a. Work closely with sales, marketing, and business analyst teams to understand data requirements and provide tailored solutions. b. Translate complex data findings into clear recommendations for leadership. 6. Advanced Visualization: a. Create interactive dashboards and visualizations using tools like Tableau, Power BI, or Python libraries (e.g., Matplotlib, Seaborn). b. Present findings and insights to stakeholders in an easy-to-understand format. 7. Experimentation and Testing: a. Conduct A/B testing to evaluate marketing strategies and sales initiatives. b. Optimize campaigns by analyzing test results and making data-driven adjustments.