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Title:  Information Analytics (IA) Data Science Engineering Manager

Job ID:  22852
Country:  Spain
City:  Madrid
Professional area:  Information Technology
Contract type:  Permanent
Professional level:  Experienced

JTI is a leading international tobacco company with operations in more than 120 countries. We’re the global owner of world-renowned brands such as Winston, the number two cigarette brand in the world, and Camel (outside the US). Our global brands also include Mevius, LD and Natural American Spirit, and we manufacture the internationally recognized Logic e-cigarette brand and Ploom Tech, a major brand in the heated tobacco category. 
 
Headquartered in Geneva, Switzerland, we employ over 40’000 people across the globe. We were recently awarded Global Top Employer for the fourth consecutive year with regional Top Employer Certification in Europe #1, Asia #1, North America #1, Africa #2 and Middle East #3. This is recognition of our outstanding talent strategy, energizing culture and commitment to learning and development. 
 
We are a member of the Japan Tobacco Group of Companies. For more information visit www.jti.com.  

The IA Data Science Engineering Manager within the Advance Analytics Team will focus on enriching JTI A.I. models, with other available data (i.e. open source) through the application of advance data preparation technics, such as vectorization, feature engineering and data correlation leveraging complex algorithms and ML models.

He/she needs to be familiar with database systems, know how to source data and design data integration processes, build Ma chine Learning models are provide accessibility of new dataset to the Advance Analytics team.

He/She works with key stakeholders from the business to identify the critical business requirements and to determine if the data currently exists or can easily integrated to better improve data science models results. Where new data is required they will work with the business to ensure that they have a clear understanding of what the vendors are offering.

The IA Data Science Engineering Manager will make sure data needed meets the minimum level of requiremnts needed to perform any Data Science pilot and any needed Enterprise Architecture standards.

Will stay informed of what data/sourcing solutions are available (e.g. for social media, IoT, Big Data, Open source) and will research suppliers and markets establishing a new asset of information that can be used tby himself and the Advanded Analytics team to support data discovery activities, predictive analytics, advanced modeling.

 

EDUCATION

• University Degree in Mathematics, Physics, Machine Learning, Information Management, or Engineering

 

WORK EXPERIENCE

• 7+ years of IT and Business professional experience (with relevant industry experience) focusing on data projects and associated services multinational FMCG organizations
• Demonstrates creativity, innovation and ethical thinking in applying solutions for the benefit of the customer/stakeholder
• Should have a strong understanding of what data sources/solutions are available and how these can best be utilized by the business.
• Expert on data science modelling, ETL, APIs and data preparation.
• Excellent written and communications skills to report back the findings in a clear, structured manner are required
• Strong interpersonal skills and ability to identify and collaborate with key stakeholders across functions
• Solid knowledge and understanding of the business and organization

 

LANGUAGES&COMPUTER SKILLS

• Fluent in English
• Proficient with computers and Microsoft Office
• Experience working with external data providers and open source crawling techniques
• Proficient in  Data Trasformation
• Proficient in Data Modeling, DataLakes, Analytics Engines (Databricks), Data Science (R,Python, DataIku)

 

MAIN AREAS OF RESPONSIBILITY

Data Preparation and Data Science Moldel  Enrichment.
• Responsible for scouting Open source and External Data providers that can contribute to JTI data science models enrichment to be fully leveraged for advanced analytics purposes.
• Will look to consolidate existing sources across functions and geographies to leverage economies of scale and facilitate data access.
• Will help defining the standards for the storage and manipulation of external data within JTI and explore third party suppliers providing "data harvesting" solutions.
• The IA Data Science Engineering Manager will work in the data science team, defining a clear data strategy to fully leverage external data as an asset.

 

Continuous Improvement
• Through the application of advance data preparation technics, such as vectorization, feature engineering, data correlation, data imputation through complex algorithms and data science models, he/she contributes positively to JTI's Data Science quality standards, helping stakholders to understand where the dependency on Global Data Suppliers can be reduced or complemented.
• Strong organizational, analytical, and decision-making skills
• Ability to anticipate and solve problems
• Energetic, innovative, and flexible
• Proven change management skills and experience

 

Market Knowledge
• Will stay informed of what data/sourcing solutions are available (e.g. for social media, IoT, Open Source, Big Data) and will research suppliers and markets, and maintain a broad understanding of the commercial environment, to inform and develop commercial strategies and sourcing plans in line with business goals
 

Communication and Collaboration
• Must work in collaboration with functional teams to understand requirements (globally and locally) and investigate external and open source data availablity and its usage in predictive modeling.
• Will work with relevant stakeholders and Enteprise architecture to develop and approve the business case (cost/benefits) of new open source data ingestion focused on the enriching of JTI’s  data science models.
• Work with the information architect to determine which standards should be applied for data integration and access.
 

Training and Support
• Working with Business teams to ensure key business users are educated and trained in what innovative sources is currently available and what external data (and data services) are available to data science modelling to add value to JTI's business cases.


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