Data Scientist - MX | IBM (2023)

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Introduction
At IBM, work is more than a job – it’s a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you’ve never thought possible. Are you ready to lead in this new era of technology and solve some of the world’s most challenging problems? If so, lets talk.

Your Role and Responsibilities
Introduction
Technology sales at IBM is evolving it’s way of working to break beyond boundaries with innovative approaches. Preferring to ‘show’ vs. ‘tell’, Client Engineering co-creates with prospective customers, in real-time, on solutions to their hardest business challenges.

As a Data Scientist within Client Engineering you’ll be a key player in a multi-disciplinary team made up of Engineers, Architects, Designers, Developers, and Business Strategists. The brightest minds collaborating with clients as one team, and contributing to experiential working sessions. The outputs of which produce minimal viable product (MVP), enterprise-scale solutions at lean, start-up speed.

Excellent onboarding training will set you up for success, whilst ongoing development will continue to advance your career through its upward trajectory. Our sales environment is fast-paced and supportive. Always part of a team, you’ll be surrounded by leaders and colleagues who are always willing to help and be helped – as you support minimal viable products (MVPs), and proofs of concept that compel clients to invest in IBM’s products and services.

Your Role and Responsibilities
As a Data Scientist within Client Engineering, you’ll be the expert advisor on machine learning (ML), optimisation, neural networks, data and AI statistical modelling, and other quantitative approaches. Applying these to business problems, you’ll work with your Solution Architect and wider team to present insights and trend-predictions that contribute to optimising value-providing solutions for prospective clients.

We’re passionate about success. If this role is right for you, then your achievements will mean that your career is flourishing and our clients are thriving. To help ensure this win-win outcome, a ‘day-in-the life’ of this opportunity may include, but not be limited to:

• Applying statistical and programming languages (R, Python, SPSS) and database languages (SQL) to evaluate and improve data sets’ quality,y and train predictive and prescriptive models.

• Applying multiple data engineering techniques, as Spark, Hive, HDFS and Data API Design, to gather, prepare, cleanse, and transform client data for analysis and AI automation.
  
• Developing partnerships at all levels within your clients to identify new opportunities for data science applications.

• Co-creating Data /AI solutions with client that leverage IBM Data & AI offerings to solve the client’s business challenges​ through a quick Prove of Experience (PoX) delivery. Provide insights to the client business case(s)

• Demonstrating strong business acumen to understand your clients’ hardest problems, formulating hypotheses and testing conclusions to influence solution designs.

Required Technical and Professional Expertise

• Essential:
  • A demonstrable deep understanding of statistics, machine learning and NLP/NLU.
    
  • Provable expertise in identifying data sources, transforming data, and using frameworks (e.g., MXNet, TensorFlow, PyTorch, SparkML, scikit-learn) that contribute to the development of client’s ML models.

• • Advanced programing skills with Python, R, Scala, Java, C++ or GO.

• • Ability to use ETL tools. Good understanding of Data Governance and Data Observability
    

Excellent communication skills at all levels with evident comfort in a client-facing role. Able to contribute to the facilitation of experiential problem discovery, framing and solutioning session.

Preferred Technical and Professional Expertise

• • Deep technical expertise and business domain knowledge to solve client business problems through Data Science.
    
  • Masters, PhD (r equivalent) in a highly quantitative field (Data Science, Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.)
    
  • Experience and ability f working across multi clouds (IBM, AWS, GCP and Azure)

• • Programing skills with Chatbots or ETLs

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