Data Scientist

Key Skills

Aartificial intelligence, Apache Hadoop, AWS Sagemaker, Computer Vision (CV), Data science techniques, Employee life cycle, Machine learning techniques, Natural Language Processing (NLP), Planning and organization, SPARK Programming, SQL

Job Description

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