Business Data Analyst

Job Title: Business Analyst Duration: 6 months with possibility to extension. Key Focus: Pharmaceutical Domain 5+ years and Data and commercials Analytics areas Overview : As a Business Analyst, you will play a pivotal role in bridging the gap between business needs and technical solutions within the life sciences commercial domain, specifically in the data analytics space. Your responsibilities will include analyzing and translating complex business requirements into actionable technical tasks for offshore teams, ensuring seamless integration of business models with technology solutions. Must Have Skills: 1. Domain Expertise: Demonstrate advanced proficiency in the Pharmaceutical domain, with a strong focus on data analytics and business processes. Leverage your expertise to assess business models and their integration with technology. 1. LS commercial Data on 852, 857 datasets - 852 – Product Activity Data: This EDI (Electronic Data Interchange) transaction set provides detailed product activity information such as inventory levels, sales, and demand data. It’s typically sent from a retailer or distributor to a manufacturer or supplier to give visibility into product movement and stock levels. Key fields include: On-hand inventory Shipments and receipts Sales or usage by location Forecasted demand 857 – Shipment and Billing Notice: This dataset (EDI 857) combines the functionality of a ship notice (ASN, 856) and an invoice (810). It communicates both shipment details and billing information in a single document. Used commonly in retail and logistics to streamline reconciliation and payment processes. Data Kitchen experience 1. Requirements Analysis: Exhibit expert proficiency in gathering, synthesizing, and analyzing business requirements. Break down business tasks into technical terms and translate them into clear, actionable deliverables for offshore teams. 1. Collaboration and Coordination: Collaborate effectively with Business Relationship Managers (BRMs), business stakeholders, vendors, and internal Accenture teams. Facilitate communication and alignment across diverse teams to drive key decisions and ensure project success. 1. Stakeholder Engagement: Build strong relationships with client stakeholders and vendors, acting as a trusted advisor to guide discussions and decisions in the commercial area. 1. Technical Translation: Translate business requirements into technical specifications, ensuring offshore teams have a clear understanding of deliverables and priorities. 1. Business Process Analysis: Utilize advanced proficiency in business process analysis tools to identify opportunities for optimization and innovation within the commercial domain. 1. Proficient in SQL Nice to Have Skills: 1. Technical Expertise: Demonstrate proficiency in 1. ETL/ Database 1. AWS 1. Red Shift 1. Tableau Basic 1. Fivetran (Optional) 1. Shellscript (Python, UNIX) (Optional) Detailed Role and Responsibilities : Roles: • Requirements Gathering: • Collect and document business requirements related to data analytics. • Ensure alignment with business objectives. • Data Interpretation: • Analyze data sets to inform technical design and decision-making. • Collaborate with data engineers to meet business needs. • Stakeholder Communication: • Act as a liaison between technical teams and non-technical stakeholders. • Facilitate alignment across teams. • Process Improvement: • Identify and recommend enhancements to business processes through data analytics. Responsibilities: • Analyzing Business Needs: • Translate business objectives into actionable data requirements. • Ensure solutions align with business goals. • Data Quality Assurance: • Ensure data accuracy and reliability for analysis. • Implement data quality standards. • Creating Documentation: • Develop user stories, use cases, and process documentation. • Maintain clear project records. • Testing and Validation: • Collaborate with QA teams to test and validate analytics solutions. • Ensure solutions are user-friendly and scalable. • Training and Support: • Provide training and support on analytics tools and insights. • Empower stakeholders to leverage data solutions. • Continuous Improvement: • Monitor project outcomes and recommend enhancements. • Stay updated on industry trends. • Collaboration: • Work closely with data engineers to align technical solutions with business needs.