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Consultant Advanced Analytics - Pharma
Consultant Advanced Analytics - PharmaDataZymes • ajmer, India
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Consultant Advanced Analytics - Pharma

Consultant Advanced Analytics - Pharma

DataZymes • ajmer, India
2 days ago
Job description

About DataZymes :

Founded in 2016, DataZymes is a next-generation analytics and data science company driving technology and digital-led innovation for our clients, thus helping them get more value from their data and analytics investments. Our platforms are built on best-of-breed technologies, thus protecting current investments while providing clients more bang for their buck. Our mission at DataZymes is to scale analytics and enable healthcare organizations in achieving non-linear, long term and sustainable growth. In a short span, we have built a high-performance team in focused practice areas, built digital-enabled solutions, and are working with some marquee names in the US healthcare industry.

Job Description :

Currently seeking a highly skilled Analytics Consultant to work with the Decision Sciences vertical. Key responsibilities include managing major projects and clients within the Pharma domain. Your expertise in analytical solutions will play a critical role in addressing our client’s business problems and driving their success.

EXPERIENCE REQUIRED :

  • 4+ years of experience in analytics or decision sciences, with a focus on the pharmaceutical industry.
  • Demonstrated experience executing analyses and supporting delivery on complex pharma analytics projects.
  • Strong expertise in commercial analytics like Market Mix Modelling, Test and Control analytics, OCE analytics, Budget Optimization etc.
  • Preferred exposure to Patient analytics, Market Access analytics, or Sales analytics.
  • Experience collaborating with cross-functional teams; informal mentoring or peer coaching is a plus.
  • Power BI or similar dashboarding experience (e.g., Tableau, Looker) is a plus.

Technical Skills :

  • Advanced knowledge of statistical analysis tools and programming languages such as Python, SAS, SQL, etc. along with a strong hold on Microsoft suit.
  • Strong understanding of AI / ML techniques and their applications in decision sciences.
  • Communication and Leadership :

  • Excellent written and verbal communication; able to translate analyses into crisp, client-ready narratives, collaborate with cross-functional teams, and build strong client relationships.
  • Demonstrates ownership and initiative; contributes to thought leadership (POVs, case studies, playbooks).
  • RESPONSIBILITIES

    Delivery & day to day operations  :

  • Support planning and day-to-day execution of Decision Sciences projects, owning defined workstreams and deliverables.
  • Partner with cross-functional teams to clarify objectives, scope, timelines, and resource needs; maintain project trackers and documentation.
  • Perform hands-on analytics, data preparation, and QC; flag risks / blocks early and propose practical mitigations.
  • Monitor progress against milestones and quality standards to ensure on-time, high-quality delivery.
  • Drive the development and enhancement of analytical products and solutions for commercial and marketing analytics.
  • Conduct market research and stay updated on industry trends, emerging technologies, and best practices.
  • Collaborate with internal stakeholders, including data scientists, software developers, and domain experts, to define product requirements.
  • Guide the product development lifecycle, from ideation and prototyping to testing, deployment, and maintenance.
  • Design, Develop, and deploy complex and innovative analytical solutions for clients.
  • Product & Solution Development :

  • Contribute to the design and enhancement of analytics solutions for commercial and marketing use cases.
  • Stay current on industry trends, tools, and best practices; synthesize learnings into actionable recommendations.
  • Build, validate, and help operationalize models, pipelines, and dashboards as part of deployment / deliverables.
  • Apply and evaluate statistical / ML techniques relevant to pharma analytics; compare methods for fit, accuracy, and interpretability.
  • Design small experiments / POCs, analyze outcomes, and summarize implications for client use.
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