Core Job Responsibilities:
The Data Scientist rapidly navigates from identifying priorities and helping to generate ideas to implementing solutions. They
- Participate/drive data collection, cleaning, analysis and interpretation (EDA).
- Collaborate with the business partner and product owners to ideate on solutions to challenging problems.
- Generate insightful visualizations to communicate findings.
- Carry out model selection, validation and possible ways for deployment (in collaboration with the engineering team).
- Write high quality code with possibility of deployment in mind.
- Share the learnings and findings with other data scientists contributing to the collaborative environment.
- Collaborate with Sr. Data Scientists and take full responsibility for analysis and modeling tasks.
- Build effective and efficient AA solutions to business needs, leveraging available market resources as much as possible.
- Keep himself/herself committed to continuous learning about the latest trends and technologies.
- Work closely with the Product Owners and the Engineering team to ensure delivery of the Data Science part of the projects within time, cost and quality.
- Collaborate with external vendors, evaluating their capabilities and ensuring their alignment with data science standards and project requirements.
- Continuously engage in hands-on data analysis, modeling, and prototyping DS frameworks to deliver high-quality outputs.
Supervisory/Management Responsibilities:
- Direct Reports: None.
- Indirect Reports: None.
Position Accountability/Scope:
The Data Scientist is responsible for delivering targeted business impact per initiative in collaboration with key stakeholders and identifying next steps/future impactful opportunities. This individual contributor role involves working with cross-functional teams to build innovative solutions for internal business functions across different geographies.
Minimum Education:
Master or PhD in relevant field (e.g., applied mathematics, computer science, engineering, applied statistics)
Minimum Experience:
- At least 3-5 years of relevant working experience, ideally in pharma environment
- Solid experience working on full-life cycle data science; experience in applying data science methods to business problems (experience in the financial/commercial or manufacturing / supply chain areas a plus).
- Strong experience in e.g., data mining, statistical modelling, predictive modelling, and development of machine learning algorithms
- Proven problem-solving ability in international settings preferably with developing markets
- Proven experience in working in cloud environment preferably AWS / Sagemaker
- Strong experience working on full-life cycle data science; experience in applying data science methods to business problems
- Practical experience in deploying machine learning solutions
- Strong understanding of good software engineering principles and best practices
- Ability to work and lead cross-functional teams to bring business and data science closer together - consultancy experience a plus
- Intrinsic motivation to guide people and make Advanced Analytics more accessible to a broader range of stakeholders
- Deep domain expertise in a specific field, such as Artificial Intelligence, Machine Learning, Natural Language Processing, or Computer Vision
- Strong programming skills in languages such as Python or R, with proficiency in data manipulation, wrangling, and modeling techniques
- Strong experience building and debugging complex SQL queries
- Excellent knowledge of statistical techniques, machine learning algorithms, and their practical implementation in real-world scenarios
- Exceptional communication and presentation skills, with the ability to convey complex concepts and insights to both technical and non-technical stakeholders
- Proven track record of delivering data-driven solutions that have had a measurable impact on business outcomes
- Exposure to big data technologies (e.g., Hadoop, Spark) is highly desirable
- Demonstrated ability to drive the adoption of data science best practices, standards, and methodologies within an organization
- Fluency in English a must, additional languages a plus