This is contract to hire opportunity with 3+ years of experience onwards., If you are interested pls share your resume to [Confidential Information] with below details.
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Job Description
Essential for the role
- Quantitative bachelor's degree from an accredited college or university is required in one of the following or related fields: Engineering, Operations Research, Management Science, Economics, Statistics, Applied Math, Computer Science or Data Science. An advanced degree is preferred (Master's, MBA or PhD).
- 3+ years of experience in application of advanced methods and statistical procedures on large and disparate datasets.
- Statistical Analysis & Modelling: Design of Experiments, Time Series, Regression, Applied Econometrics and Bayesian methods.
- Data Mining, Predictive Modelling & Machine Learning algorithms.
- Optimization & Simulation.
- 3+ years of recent experience and proficiency with PySpark, Python, R, SQL.
- Working knowledge of data visualization PowerBI, MicroStrategy, Tableau, Qlikview, D3js or similar tools.
- Working knowledge on platforms like DataBricks.
- Experience in MS Office products - Excel and PowerPoint skills required.
- Expertise in managing and analyzing a range of large, transactional databases is required.
- Statistical analysis and modelling background
- ML a plus
- Experience with IQVIA data sets.
- Ability to derive, summarize and communicate insights from analyses.
- Organization and time management skills.
Desirable
- Strong leadership and interpersonal skills with demonstrated ability to work collaboratively with a significant number of business leaders and cross-functional business partners.
- Strong communication and influencing skills with demonstrated ability to develop and effectively present succinct, compelling reviews of independently developed analyses infused with insight and business implications/actions to be considered.
- Strategic and critical thinking with the ability to engage, build and maintain credibility with Commercial Leadership Team.
- Strong organizational skills and time management; ability to manage diverse range of simultaneous projects.
- Knowledge of AZ brand and Science (Oncology in particular).
- Experience using Big Data, is a plus. Exposure to SPARK is desirable.
- Should have Excellent Analytical, Problem-Solving ability. Should be able to grasp new concepts quickly
Ideal Candidate Summary Brand Analytics / Data Analyst (37 yrs)
- The ideal candidate is a data-driven analytics professional with 37 years of hands-on experience in advanced statistical modelling, predictive analytics, data mining, and machine learning, preferably within the pharma or commercial analytics domain. They should have strong expertise in Python, PySpark, R, SQL, and experience working with large, complex datasets, especially IQVIA or other healthcare datasets.
- They bring a strong foundation in statistical methods (regression, time series, DOE, Bayesian approaches), along with experience in optimization, simulation, and building predictive/prescriptive models. The candidate is comfortable working on platforms like Databricks and creating impactful visualizations using PowerBI, Tableau, Qlik, or similar tools.
- They function as a proactive internal consultant, partnering closely with Marketing, Sales, Medical, Market Access, and Commercial teams to drive customer segmentation, targeting, ROI analysis, forecasting support, HCP/patient analytics, resource allocation, and market simulations. They excel at converting complex analysis into clear business insights and influencing stakeholders through strong storytelling and presentation skills.
- The ideal candidate demonstrates strategic thinking, excellent communication, strong business acumen, and the ability to manage multiple analytical projects simultaneously. Experience in pharma/oncology, exposure to big data technologies (Spark), and knowledge of US commercial datasets is a strong plus. They stay current with new analytical techniques, champion continuous improvement, and contribute to elevating analytic capabilities across the organization.
