Technology sales at IBM is evolving its 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.
As a Data Scientist within Client Engineering, you'll be the expert advisor on machine learning (ML), optimization, 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 optimizing value-providing solutions for prospective clients.
Your primary responsibilities will include:
Data Analysis: Apply statistical and programming languages (R, Python, SPSS) and database languages (SQL) to assess and enhance the quality of data sets, as well as to develop predictive and prescriptive models.
Data Engineering: Utilize multiple data engineering techniques such as Spark, Hive, HDFS, and Data API Design to collect, prepare, cleanse, and transform client data for analysis and AI automation.
Client Relationship Building: Establish partnerships with clients at all organizational levels to identify new opportunities for data science applications.
Solution Co-Creation: Collaborate with clients to co-create Data/AI solutions that leverage IBM Data & AI offerings to address their business challenges through rapid Proof of Experience (PoX) delivery. Provide insights to strengthen the client's business cases.
Business Acumen: Demonstrate a strong understanding of clients' most complex issues, formulate hypotheses, and test conclusions to shape solution designs.
Data Science Expertise: A deep understanding of statistics, machine learning, and natural language processing/understanding (NLP/NLU).
Data Handling: Expertise in identifying data sources, transforming data, and using frameworks such as MXNet, TensorFlow, PyTorch, SparkML, and scikit-learn to contribute to the development of client's machine learning models.
Programming Proficiency: Advanced coding skills in Python, R, Scala, Java, C++, and GO.
ETL and Data Governance: Proficiency in using ETL (Extract, Transform, Load) tools, along with a good understanding of Data Governance and Data Observability.
Communication Skills: Excellent communication skills at all levels, with a demonstrated comfort in client-facing roles. Capable of contributing to the facilitation of experiential problem discovery, framing, and solutioning sessions.
- Undergraduate students who will graduate in December 2026 or later.
- Advanced in English and Spanish (oral and written).
- Studying Computer Engineering or related areas.
- Experience (academic, personal or professional projects) as a Developer/Data Scientist.
- Excellent communication, presentation and interpersonal skills.
- Proactivity, collaboration and willingness to learn as part of a team.