Data Scientist

A career in IBM Consulting is rooted by long-term relationships and close collaboration with clients across the globe.
You'll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio, including Software and Red Hat.
Curiosity and a constant quest for knowledge serve as the foundation to success in IBM Consulting. In your role, you'll be encouraged to challenge the norm, investigate ideas outside of your role, and come up with creative solutions resulting in groundbreaking impact for a wide network of clients. Our culture of evolution and empathy centers on long-term career growth and development opportunities in an environment that embraces your unique skills and experience. Your Role and Responsibilities The Data Scientist role combines deep data and analytics skills with strong business acumen to solve business problem of Cognitive Computing Solutions. The responsibilities include working with business leaders to solve business problems by understanding, preparing, and analysing data to predict emerging trends and provide recommendations to optimize business resultsThe data scientist is expected to have expertise of Advanced Analytics techniques that are traditionally applied to structured data, as well as deep understanding of Natural Language Processing and Machine Learning techniques such as Deep Learning and GenAI for unstructured content, so as to enable composition of holistic cognitive solutions. The candidate is expected to be well versed in one of the following areas of Natural Language Processing, Image Processing, Video Processing, Voice Processing, Q&A assistant solutions and other forms of GenAI solutions. The candidate is expected to have knowledge and/or experience in the following skills with focus on Data Science:Machine Learning, Git, Agile, SQL, Python, R, Predictive Modelling, Algorithms, Web Scraping, TensorFlow, Deep Learning, Statistics and Natural Language Processing / GenAI and Agentic AI. This will include the following:

Experience of formulating a problem statement and implementing analytical solutions by understanding available data and functional requirements.

High proficiency in algorithms used for structured and unstructured data analysis

Expert-level proficiency in statistical/ML predictive techniques such as Regression, Random Forest, Decision Tree, SVM etc or Experience in Probabilistic techniques, Reinforcement learning, Deep Learning- RNN, LSTN, NLP and Interactive AI (Chatbots)

Expert-level proficiency in Foundational s models / LLMs, Lang chain, Vector DB, Python or data science stack.

Operating knowledge in any one of the of cloud computing platforms (IBM, AWS, Google, Azure) and its data science stack

RDBMS and data management concepts as well as fluency in SQL scripting.

Experience in implementing distributed computing using Python

Experience in Agile Delivery and version management

The ideal candidate will have 7-12 years of experience in designing, GenAI, Deep Learning, ML/statistical solutions to complex business problems at scale; developing & testing modular, reusable, efficient and scalable code to implement those solutions. The skills include strong coding, statistical, mathematical, predictive modelling, and business strategy skills and will need to be able to effectively communicate orally and visually -- what all of the data means in the context of our business.

Required Technical and Professional Expertise

BTech (with 7+ years of relevant experience) or Masters (with 6+ years of relevant experience) in Operations Research, Applied Mathematics/ Statistics/ Econometrics, Electrical or Systems Engineering, Physics or similar highly quantitative field

Strong ability to transform business requirements into data science formulations and implement the solutions in an efficient and scalable fashion

Sound understanding of data science concepts, model development & performance tuning processes as well as coding, version control and CI/CD best practices

Demonstrated extensive experience in building and deploying production quality models in a live digital environment using data pipelines and ML Ops frameworks including handling model drift, retraining and version control lifecycle

Highly skilled in Python and various data science related libraries of Python including TensorFlow, Keras, Sci-Kit Lean, Pandas, Numpy and PySpark

Experience in Convolutional Neural Network / Computer Vision projects using TensorFlow, PyTorch and leveraging public / open-source libraries (VGG16, ImageNet, YOLO, OpenCV, etc) as well as ability to tweak, modify these CNN architectures when required for a specific business problem.

Demonstrated ability of scoping, executing and scaling multiple data science deliverables on their own

Must have worked in developing and deploying models using more than one cloud platform (AWS, Azure, GCP, IBM)

Ability to handle multiple projects as an individual contributor and as a lead / mentor to other team members managed directly or indirectly on a project / assignment

Excellent interpersonal and stakeholder management skills including ability to interact and present to senior stakeholders

Preferred Technical and Professional Expertise

None