Job Specifications
Introduction
At IBM, we're revolutionizing our approach to technology sales. Our Client Engineering teams are champions of co-creating solutions in real-time to solve complex business challenges.
As a Data Scientist within Client Engineering, you'll be the expert advisor on Machine Learning (ML) and Generative AI solutions, 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.
At IBM the possibilities are endless. We offer extensive onboarding and ongoing development, fostering an environment where you can thrive and shape your own career trajectory. Surrounded by a supportive team, you'll be integral in creating user-centric, compelling pilots that lead clients to continually invest in IBM's people, products, and services.
Your Role And Responsibilities
Your primary responsibilities will include:
AI Solution Co-Creation: Translate complex client challenges into impactful, scalable AI solution architectures. Prototype and explore innovative Machine Learning and GenAI solutions by developing Proofs-of-Concepts (POC) to rapidly test and prove transformative solutions for clients.
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.
Business Acumen: Demonstrate a strong understanding of clients' most complex issues, formulate hypotheses, and test conclusions to shape solution designs.
Industry Trends and Innovation: Stay up to date with the latest trends and advancements in AI, machine learning, foundation models, and large language models. Evaluate emerging technologies, tools, and frameworks to assess their potential impact on solution design and implementation.
Preferred Education
Master's Degree
Required Technical And Professional Expertise
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.
Strong organizational, planning, and team collaboration skills with the ability to work on multiple projects at the same time.
Generative AI expertise: Proficient in advancements in Large Language Models (LLM), Generative AI frameworks, Agentic AI, and related technologies.
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.
Strong programming skills: Proficiency in Python and experience with AI frameworks such as TensorFlow, PyTorch, Keras or Hugging Face. Understanding in the usage of libraries such as SciKit Learn, Pandas, Matplotlib, etc. Familiarity with cloud platforms (e.g. Kubernetes, AWS, Azure, GCP) and related services is a plus.
Preferred Technical And Professional Experience
AI-related education: Possess a Master's, or Ph.D. degree in a highly quantitative field such as Data Science, Computer Science, Machine Learning, Operational Research, Statistics, or Mathematics.
Data lineage: Experience with modeling a record of data throughout its lifecycle, including source information and any data transformations during any ELT or ETL process.
Data fabric: Experience with implementing a data fabric solution in a hybrid cloud architecture.
Multi-cloud experience: Demonstrate the ability to work across multiple clouds, including IBM, AWS, GCP, and Azure.
Comprehensive familiarity with IBM's offerings: Hands-on experience with any of IBM's products and services (training across IBM's product suite will be provided).