Data Scientist

Job Description Summary

This person as Data Scientist, will be responsible for use machine learning techniques and statistical modelling to deliver actionable, data-driven insights to help business franchise better understand HCPs and how to market to them. In addition, combine unrivalled external and internal data sets to solve problems across sectors such as medical, marketing, sales, and other related departments in Novartis Japan.

Job Description

Major Accountabilities (Describe the 5-7 main results of this role to be achieved)

• Considering business issues against the current processes and assisting in the development and / or implementation of solutions utilizing analysis techniques

• Propose data science problem solving approaches and co-create approaches to meet stakeholder needs

• Analyze, cleanse and visualize data to identify trends and make decisions

• Formally documenting functions and processes using PowerPoint

• Meeting with stakeholders to understand and review business objectives and project requirements

• Understand the different perspectives of multiple stakeholders whilst being comfortable to challenge where appropriate

Key Performance Indicators (Indicate how performance for this role will be measured)

• Customer satisfaction evaluation

• Customer satisfaction rating / feedback by internal stakeholders (Business Franchise Head, relevant management members, and brand marketing team)

• Analytics projects resulting in better decision making or tangible actions

• Literacy improvement as well as culture to make fact & data-driven decision making

Background (State the required education, experience level, and competency profile)

Education:

• Academic background in a relevant quantitative field, e.g. statistics, engineering, computer science, with an advanced degree (MSc) in one of these disciplines preferred.

Experience/Professional requirement:

• Experience providing advanced analytics as a Data Scientist in pharmaceutical industry, medical or other front office context
• Strong technical expertise in data science, statistical modelling and/or machine learning
• Fluency with Python machine learning and / or R and data science packages (Pandas, Numpy, Scikit-learn, Tensorflow, etc.)
• Professional record demonstrating business impact; domain experience in areas such as pharmaceutical industry, real world evidence, customer analytics, CRM, and digital marketing will be beneficial.
• Excellent documentation, communication and presentation skills, including the ability to explain and present the findings of technical work to non-expert audiences
• Excellent communication and stakeholder management skills and ability to integrate well into a team and build effective relationships
• Demonstrated experience in effectively leading and managing cross-functional teams to achieve project milestones and deliverables.
• Proven ability to foster a collaborative and motivated team environment, ensuring open communication and knowledge sharing among team members.
• Proven ability to collaborate effectively with global teams
• Previous track record of successfully delegating tasks, setting performance expectations, and providing constructive feedback to team members.
• Strong interpersonal skills with the ability to resolve conflicts, promote employee engagement, and maintain a positive work culture.
• Substantial leadership experience in guiding teams through complex projects, making critical decisions, and adapting to changing priorities.
• Proven ability to inspire and influence others towards achieving organizational goals, fostering a sense of ownership and dedication.
• Proven expertise in leading teams through organizational changes, demonstrating resilience and adaptability in the face of shifting priorities.

English Skill:

• Business level is required

Skills Desired

Biostatistics, Computer Programming, Data Analysis, Databases, Data Management, Data Mining, Data Quality, Data Visualization, Deep Learning, Graph Algorithms, High-Performance Computing, Logistic Regression Model, Machine Learning (Ml), Master Data Management, Pandas (Python), Python (Programming Language), R (Programming Language), Random Forest Algorithm, Sql (Structured Query Language), Statistical Modeling, Time Series Analysis