4 Omnichannel Analytics Jobs

At PwC, our team in data and analytics engineering focuses on utilizing advanced technologies and techniques to design and develop robust data solutions for clients. You will be instrumental in the process of transforming raw data into actionable insights, facilitating informed decision-making, and propelling business growth. If you are in data science and machine learning engineering at PwC, your primary focus will be on harnessing advanced analytics and machine learning techniques to extract insights from extensive datasets and drive data-driven decision-making. Your responsibilities will include developing predictive models, conducting statistical analysis, and creating data visualizations to address complex business challenges. In this role, you will have a vital role in organizing and maintaining proprietary datasets, converting data into insights and visualizations that steer strategic decisions for both our clients and the firm. Your collaboration will be key with industry leaders and various cross-functional retail and consumer advisory, tax, and assurance professional teams to develop impactful, commercially relevant insights. These insights will be integrated into thought leadership, external media engagement, demand generation, client pursuits, and delivery enablement initiatives. Key Skills Preferred: - Demonstrates in-depth abilities and a successful track record in managing efforts to identify and address client needs. - Work as a crucial member of a team of Retail and Consumer data scientists, analyzing large, complex datasets to reveal insights on consumer sentiment, future business trends, challenges, consumer-related events, business strategy, pricing, promotions, customer segmentation, and supply chain optimization. - Assist in the discovery of innovative datasets that contribute to the firm's competitive edge among rivals and clients. - Aid in constructing predictive models and data-driven tools. - Plan and execute experiments such as A/B testing, market basket analysis to evaluate the effectiveness of new approaches and drive continuous improvement. - Collaborate with the US team to translate analytical findings into actionable recommendations and compelling narratives. - Develop dashboards and reports using tools like Tableau, Power BI, or Looker to facilitate self-service analytics and decision-making. - Stay updated on industry trends, customer behavior patterns, and emerging technologies in the consumer and retail sector. - Proficiency in managing high-performing data science and commercial analytics teams. - Strong skills in SQL and Alteryx, and proficiency in Python and/or R for data manipulation and modeling. - Experience in applying machine learning or statistical techniques to real-world business challenges. - Sound understanding of key retail and consumer metrics such as CLV, churn, sales velocity, basket size, etc. - Ability to explain complex data concepts to non-technical stakeholders effectively. - Familiarity with retail and consumer datasets like Circana, Yodlee, Pathmatics, Similar Web, etc. - Knowledge of geospatial or time-series analysis in a retail context. - Previous involvement in pricing optimization, inventory forecasting, or omnichannel analytics.,

At PwC, the focus of individuals in data and analytics engineering is to utilize advanced technologies and techniques for designing and developing robust data solutions for clients. They are instrumental in converting raw data into actionable insights, facilitating informed decision-making, and propelling business growth. Those specializing in data science and machine learning engineering at PwC concentrate on employing advanced analytics and machine learning techniques to extract insights from extensive datasets and drive data-driven decision-making. Your responsibilities will include developing predictive models, conducting statistical analysis, and creating data visualizations to tackle intricate business challenges. You will have a significant role in organizing and maintaining proprietary datasets, transforming data into insights and visualizations that steer strategic decisions for both clients and the firm. Working closely with industry leaders and various cross-functional retail and consumer advisory, tax, and assurance professional teams, you will assist in developing impactful, commercially relevant insights to integrate into thought leadership, external media engagement, demand generation, client pursuits, and delivery enablement. Preferred Knowledge and Skills: Demonstrates in-depth abilities and a proven track record of success in managing efforts to identify and address client needs: - As a critical member of a team of Retail and Consumer data scientists, you will maintain and analyze large, complex datasets to uncover insights related to consumer sentiment, future business trends/challenges, cyclical consumer events (e.g., holidays, back-to-school, Super Bowl), business strategy, pricing, promotions, customer segmentation, and supply chain optimization. - Assist in identifying new, cutting-edge datasets to enhance the firm's differentiation among competitors and clients. - Assist in building predictive models and data-led tools. - Design and implement experiments (e.g., A/B testing, market basket analysis) to assess the effectiveness of new approaches and drive continuous improvement. - Collaborate with the US team to translate analytical findings into actionable recommendations and compelling narratives. - Develop dashboards and reports using tools like Tableau, Power BI, or Looker to support self-service analytics and decision-making. - Stay abreast of industry trends, customer behavior patterns, and emerging technologies in the consumer and retail landscape. - Experience in managing high-performing data science and commercial analytics teams. - Strong SQL and Alteryx skills, proficiency in Python and/or R for data manipulation and modeling. - Experience in applying machine learning or statistical techniques to real-world business problems. - Solid understanding of key retail and consumer metrics (e.g., CLV, churn, sales velocity, basket size). - Proven ability to explain complex data concepts to non-technical stakeholders. - Experience with retail and consumer datasets such as Circana, Yodlee, Pathmatics, Similar Web, etc. - Knowledge of geospatial or time-series analysis in a retail setting. - Previous work involving pricing optimization, inventory forecasting, or omnichannel analytics.,

You will be responsible for driving the digital transformation of Biocon Biologics" global marketing communications as the MarTech, Omnichannel & DAM Lead. In this role, you will lead the MarTech strategy, omnichannel enablement, and digital asset management (DAM) for the company. Your main focus will be on planning, designing, and developing digital promotional materials that seamlessly integrate with global MarTech platforms and omnichannel engagement frameworks. Additionally, you will manage the GMCs Digital Asset Management (DAM) system - BrandSphere, ensuring efficient storage, access, and governance of marketing content. Your key responsibilities will include: - Developing and executing the MarTech roadmap for GMC, aligning it with Biocon Biologics" global business objectives. You will identify, evaluate, and implement advanced digital marketing tools to enhance promotional effectiveness and engagement. You will also drive automation, AI, and data-driven marketing initiatives to optimize digital interactions with healthcare professionals and other stakeholders. - Leading the creation and deployment of compliant digital promotional materials tailored for pharma and biosimilar marketing. This includes eDetailers, RTEs, webinars, interactive PDFs, videos, etc. You will collaborate with creative and content teams to ensure high-quality digital execution and utilize existing Martech infrastructure to drive stakeholder engagement. - Implementing a global multichannel strategy for GMC to ensure a seamless customer experience across multiple digital and offline touchpoints. You will enable personalized, data-driven engagement with stakeholders and ensure effective integration of digital channels such as web, email, social media, webinars, and rep-triggered engagements. Developing KPIs to track campaign effectiveness, customer engagement, and channel performance. - Leading the management of GMCs Digital Asset Management (DAM) system (e.g., GMC BrandSphere) to ensure proper storage, accessibility, and governance of all marketing materials. You will define DAM taxonomy, metadata structures, and access control mechanisms to enhance asset retrieval and compliance. Establish governance frameworks for content approvals, version control, and lifecycle management. - Collaborating with regional and country marketing teams to tailor omnichannel strategies and DAM usage to local market needs. You will maintain brand consistency across all assets while allowing for regional adaptations and work closely with compliance teams to ensure regulatory adherence across all digital initiatives. To be successful in this role, you should have 10+ years of experience in Pharma/Biosimilars digital marketing, omnichannel strategy, MarTech leadership, or DAM management. An MBA/PGDM in Marketing, Digital Marketing, or a related field is required, and a background in Life Sciences is a plus. You should have a strong understanding of pharma HCP engagement models, omnichannel activation, and digital content strategies. Expertise in CRM, marketing automation, omnichannel analytics, AI-driven personalization, and DAM systems is essential. You should also have a proven ability to lead cross-functional teams and drive digital transformation in a regulated environment.,

At PwC, the focus of individuals in data and analytics engineering is on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. Playing a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at PwC will concentrate on leveraging advanced analytics and machine learning techniques to extract insights from large datasets, driving data-driven decision making. You will be involved in developing predictive models, conducting statistical analysis, and creating data visualizations to solve complex business problems. You will be crucial in organizing and maintaining proprietary datasets, transforming data into insights and visualizations that drive strategic decisions for clients and the firm. Working closely with industry leaders and various cross-functional retail and consumer advisory, tax, and assurance professional teams to develop high-impact, commercially relevant insights for thought leadership, external media engagement, demand generation, client pursuits, and delivery enablement. Demonstrates in-depth level abilities and/or a proven record of success in managing efforts with identifying and addressing client needs: - As a critical member of a team of Retail and Consumer data scientists, maintaining and analyzing large, complex datasets to uncover insights that inform topics such as consumer sentiment, future business trends/challenges, insights around cyclical consumer-related events (e.g., holidays, back-to-school, Super Bowl, etc.), business strategy, pricing, promotions, customer segmentation, and supply chain optimization. - Supporting in the identification of new, cutting-edge datasets that add to the firm's differentiation amongst competitors and clients. - Supporting in building predictive models and data-led tools. - Designing and conducting experiments (A/B testing, market basket analysis, etc.) to measure the effectiveness of new approaches and drive continuous improvement. - Partnering with the US team to translate analytical findings into actionable recommendations and compelling stories. - Developing dashboards and reports using tools like Tableau, Power BI, or Looker to support self-service analytics and decision-making. - Staying up to date and ahead of industry trends, customer behavior patterns, and emerging technologies in the consumer and retail landscape. - Having experience managing high-performing data science and commercial analytics teams. - Strong SQL and Alteryx skills and proficiency in Python and/or R for data manipulation and modeling. - Experience applying machine learning or statistical techniques to real-world business problems. - Solid understanding of key retail and consumer metrics (e.g., CLV, churn, sales velocity, basket size, etc.). - Proven ability to explain complex data concepts to non-technical stakeholders. - Experience with retail and consumer datasets such as Circana, Yodlee, Pathmatics, Similar Web, etc. - Knowledge of geospatial or time-series analysis in a retail setting. - Prior work with pricing optimization, inventory forecasting, or omnichannel analytics.,