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- Vacancy details:
- Department :Senior Data Scientist, Sterile Injectables Design
- Qualification: Doctorate degree (Ph.D.) in Data Sciences, Chemical Engineering, Mechanical Engineering, Pharmaceutical Engineering, Physics or Physical Sciences, or related discipline.
- Experience – 3-5 years of experience
- Salary: ₹ Not Disclosed
Job Description: Greetings from Pfizer Limited ....!!
- Important Details :
- Location: India – Chennai
- Post of date:07/06/2024
- Selection Process: The selection will be on the basis of Interview.
- Mode of Interview: Face to face
- Interview Rounds of Interview: HR
ROLE RESPONSIBILITIES
The Senior Data Scientist, SID Predictive Sciences provides leadership, guidance, and alignment for the development and deployment of end-to-end Predictive Sciences and the adoption and implementation of these predictive tools within early and late-stage programs to effectively support the SID / PSSM business strategy.
The incumbent is responsible for the following key activities:
- Supporting and coordinating activities for SID Predictive Sciences by orchestrating activities across four work streams that include Digital Design, Digital Lab, Digital Manufacturing and Data Core.
- Work with Pfizer scientists to develop understanding of material properties, dosage form identification, stability, and manufacturing processes used in SI dosage form design and manufacture (liquids, suspensions, emulsions, lyophilized products, combination products, etc.) and apply material characterization and computational tools in support of formulation and process design, optimization, and troubleshooting, and dosage form quality attributes.
- Coordinate activities to identify opportunities to use engineering knowledge and computational tools to help solve complex project challenges. Be able to work in matrix environment supporting a team of scientists with minimal supervision, to define the problem statement, then develop and execute a modeling work plan to solve the issue.
- Using a variety of computational techniques, develop predictive tools and models to support drug development/manufacturing efforts for the Pfizer portfolio. Tools and Techniques include (but are not limited to): Process models and flowsheets, advanced statistical techniques, various AI/ML models and techniques, and first-principles engineering models.
- Partner and influence Sterile Injectable Design teams through late-stage project and co-development milestones to incorporate design thinking into program activities, including clinical (when appropriate) and commercial formulation identification and process selection, stability risk assessments, products manufacturing, technology transfer to commercial sites, and/or registration activities. Communicate and manage technical and business risks to senior management.
- Work with global colleagues / leaders to promote application of computational models and digital tools in SI products development workflows.
- Drive the development of state-of-the-art computational modeling and characterization tools. Summarize, communicate, and/or present results internally and externally at leading scientific conferences and scientific journal publications. Present scientific results to a diverse audience through engaging data analysis and visualizations.
- Work with external partners (e.g., universities, research organizations) and vendors (e.g., software companies) to execute and deliver well-defined modeling or characterization projects.
- Develop and deploy Predictive / Data Science workflows across SID in support of project objectives. Develop and maintain best practices for digital activities in SID through a network of key internal and external experts. Report regularly to SID Leaders / Digital Sponsors.
- Establish long-term and short-term objectives for the projects he/she leads.
- Establish an effective framework that will support and ensure the broader adoption of Predictive / Data Sciences and Solutions within SID workflows. Partner with SID leaders in securing the right resources to integrate Digital accelerators within the respective workflows.
BASIC QUALIFICATIONS
- Master’s degree in Data Sciences with relevant educational background in Chemical engineering, Mechanical Engineering, Pharmaceutical Engineering, Physics or Physical Sciences, or related discipline, with relevant educational background in computational modeling.
- Strong interest and Hands-on experience in key technical areas, including process analytics and control, data science, advanced multivariate data analysis, machine learning and artificial intelligence. Relevant hands-on industrial experience (3- 5 years, preferably pharma) in providing solutions to complex technical problems applying scientific approaches.
- Familiarity with and understanding in leveraging modeling and simulations for process development and tech transfer needs. Demonstrated initiative in developing and implementing innovative solutions. Be comfortable being hands-on and taking a deep dive into novel technical areas when required, to achieve broader objectives.
- Working knowledge of data analytical approaches. Familiarity with industrial process control, automation systems and advanced process control platforms (including DeltaV and ASPEN). Familiarity with data historian platforms (such as OSI PI), and real-time communication protocols (such as OPC, ODBS and MQTT)
- Proficient in one or more of the following programming environments: Python, R, SQL, Matlab, JAVA, Spark for data analysis, scripting, and automation. Experience with data manipulation, data cleaning and integration. Familiarity with key Data Science software suites (like Dataiku, Snowflake, Git, Tibco or Spotfire) and cloud-based platforms.
- Familiarity with Big Data analytics and control platforms (such as SIMCA, Statistica, Minitab).
- Expertise in Artificial Intelligence algorithms, such as Deep Learning, Neural Networks, and latent variable modelling for time series, root cause analysis and anomaly detection
- Comfortable taking a deep dive into novel technical areas when required, to achieve broader objectives. Creative, innovative, and dynamic. Has an excellent synthetic and analytical mind and an aptitude for problem solving.
- Good listener, understanding the needs of non-engineer internal customers. Excellent oral communication, technical writing, and interpersonal skills.
- Good teamwork skills and ability to work with wide range of technical teams and projects, internal and external partners.
- Fluent in English.
PREFERRED QUALIFICATIONS
- Doctorate degree (Ph.D.) in Data Sciences, Chemical Engineering, Mechanical Engineering, Pharmaceutical Engineering, Physics or Physical Sciences, or related discipline.
- Ability to influence and collaborate with peers. Demonstrated ability to coach / interact with others colleagues to achieve meaningful outcomes and create business impact.
- Ability to lead projects in strong collaboration with Digital and Business Partners across Pfizer.
- Excellent communication skills and the ability to indirectly influence key partners and stakeholders.
Work Location Assignment: Flexible
Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.
Research and Development
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