Data Analytics & Insights, Data Scientist, Associate Director,

Data Analytics & Insights, Data Scientist, Associate Director,

Required Qualifications

• At least 5 years of broad experience in the domains of Advanced Data Analytics, Data Science, Machine Learning, and AI in the Sales and Marketing domain.
• Knowledge and experience with best practices and frameworks utilized in the fields of Advanced Data Analytics and Data Mining.
• Self-starter, able to clearly understand business requirements and translate them into data-driven processes and solutions, as well as to consult business stakeholders on optimal solutions, and manage expectations.
• Experience with leading, mentoring, nurturing, and supporting Junior Data Scientists' team members.
• Experience with designing analytical solutions, as well as storytelling and presentation of insights and outcomes to senior business stakeholders (Directors and/or Senior Managers).
• Experience in summarizing his / her own analytical processes and results in a PowerPoint, by using diagrams, charts, etc., and explaining them to people with limited knowledge of Data Science.
• Hands-on experience within the end-to-end Advanced Data Analytics process, from data extraction, EDA, data preparation, and preprocessing, to predictive model building, validation, and deployment.
• At least 5 years of strong, hands-on programming experience with Python, as a Data Scientist, using libraries like NumPy, SciPy, Pandas, Matplotlib, SciKit-Learn, Seaborn, TensorFlow, Keras, PyTorch, as well as constructing classes, functions, and objects.
• Hands-on experience with modern Data and Analytics technologies, ecosystems, cloud environments, and services, such as AWS (SageMaker, S3, Glue, Step Functions, etc.), Azure or GCP, as well as an understanding of Big Data foundations.
• Ability to extract, design, and link datasets from diverse internal and external data sources, by writing SQL queries as a part of the ETL process, and preparing master datasets for modeling and analysis.
• Superior analytical maturity to provide business outcomes with proficiency in a broad range of marketing methodologies, quantitative approaches, Advanced Data Analysis, and statistics.
• Strong interpersonal and communications skills.
• Ability to lead data-driven initiatives, which sometimes include changing existing systems, operations or mindsets of others, based on prioritization.
• Native-level of Japanese proficiency, and business-level or higher English proficiency.
• At least 3 years of experience in actively communicating (e.g., through presentations, etc.) with business stakeholders in the Sales and Marketing / Commercial domain.
• Degree(s) in Computer Sciences, Statistics, Physics, Engineering, Mathematics, Economics, Public Health, (Pharmaco-)Epidemiology, Pharmacy.

Preferred Qualifications

• Proactive, results-oriented, creative, and flexible thinker with a growth mindset.
• Enjoys working in diverse and inclusive global and matrix environments across functions and geographies.
• Ability and willingness to speak up and challenge the status quo.
• Upholds high standards of ethical and intellectual integrity.
• Experience or knowledge about the pharmaceutical commercial business and data sources (e.g., prescription and sales data, patient claims data, promotional data), and the ability to design a future ideal state of Data Analytics in it.
• Experience in validating the quality or reliability of data.
• At least 2 years of experience in the consulting business.
• At least 2 years of experience in writing distributed processing in PySpark for Big Data Analytics solutions (e.g., datasets with millions of rows and hundreds of columns).
• Experience working in agile methodologies and agile teams.
• Experience in working in an uncertain environment, where various tasks are required to be performed simultaneously.
• Experience in completing a project, even though the direction of the project changed significantly and multiple times, due to feedback from business stakeholders.
• Experience in modifying the predictive model in a speedy manner, to incorporate important, additional business requirements, even if such a modification may result in a model which is not the best from a Data Science point of view.
• Experience in solving problems for which there is no clear answer.
• Ability to translate abstract problems from business owners into concrete issues to solve, to enumerate elements toward goals, and to derive satisfactory outcomes.
• Mathematical knowledge that is used in Data Science (calculus, linear algebra, equivalent to Japan Statistical Society Certificate level 2).
• Experience in Natural Language Processing (e.g., morphological analysis using MeCab, managing projects using BERT with Japanese data).
• Experience in Deep Learning frameworks (e.g., TensorFlow, PyTorch).
• Experience in building recommendation engine models (e.g., collaborative filtering, cosine similarity, matrix factorization models).