BeiGene
Location
San Mateo, CA | United States
Job description
General Description:
The Advanced Analytics team at BeiGene is seeking an Associate Director, Data Science with a passion for crafting and implementing predictive modeling and statistical analyses to develop comprehensive solutions and insights that directly impact patient lives and propel BeiGene's growth as a data-driven organization.
As a thought partner to the business, you will grasp the organization's strategic goals and leverage your expertise to uncover actionable insights that drive business decisions and patient benefits. Your rigorous analytical expertise will serve as a compass and decision support system for the enterprise, guiding BeiGene's path forward.
Our dynamic, collaborative team comprises subject-matter experts with diverse backgrounds and experiences, including data science, digital analytics, predictive analytics, and consulting. We foster a culture of rapid iteration, open feedback, and constructive debate, enabling us to achieve exceptional results efficiently.
We believe that data science is a team effort, yet we also encourage independent decision-making and smart risk-taking. Your ability to balance collaboration with individual initiative will be crucial for our success.
Essential Functions of the job:
Leverage deep dives into large-scale data to identify pivotal insights that will shape future product and brand strategies for targeted therapeutic areas.
Collaborate effectively with cross-functional teams to pinpoint new growth opportunities, define data requirements, establish critical metrics, and champion the adoption of data-driven products.
Design, deploy, and evaluate well-structured experiments that illuminate pathways to increased adoption, enhanced business performance, and improved patient experiences.
Conduct hypothesis-driven exploratory analyses, select appropriate machine learning algorithms, and construct sophisticated optimization engines to deliver impactful data-driven solutions.
Engage in ongoing research into emerging technologies and methodologies across data science and data engineering to elevate the team's technical capabilities.
Communicate insights to senior management with clarity and precision, distilling complex analyses and concepts into concise business-focused takeaways.
Required Educational:
Required Qualifications:
Master's degree in a relevant field with 6+ years of data analytics experience or a PhD in a relevant field with 4 + years of data analytics experience
8 plus years of overall industry experience
Technical Expertise:
Extensive experience in data mining, machine learning, and statistical analysis techniques
In-depth knowledge of ML algorithms for classification, regression, clustering, and anomaly detection
Expertise in various statistical methodologies, including Bayesian and non-parametric techniques, hypothesis testing, ANOVA, Regression, fixed and random effects
Experience working with big data environments (Hadoop, Spark) and manipulating/analyzing complex clickstream or unstructured data using Python/SQL
Practical understanding of relational databases (SQL) and large-scale distributed systems (Hadoop, Snowflake, Databricks)
Proven mastery of programming languages such as Python, Scala, Java, or R
Business Acumen:
Demonstrated ability to extract significant business insights from data and identify patterns
Proven expertise in communicating complex quantitative analyses to senior business executives
Strong leadership skills, including influencing and collaborating with peers, developing and coaching others, and overseeing the work of colleagues to achieve meaningful outcomes and create business impact
Additional Qualifications:
Experience in developing and deploying data science pipelines and applications
Ability to work independently and as part of a team
Exceptional problem-solving and analytical skills
Strong communication and presentation skills
Supervisory Responsibilities:
Travel:
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
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