IBM WATSONX, CLOUD & ENTERPRISE AI SOLUTIONS
Skills to Put on Resume for Data Scientist at IBM
IBM watsonx.ai, Granites Models, PyTorch, MLOps, Explainable AI (XAI) & Enterprise Analytics
IBM Data Science Philosophy:
IBM evaluates Data Scientists on enterprise-grade AI modeling, hybrid cloud scalability
(watsonx / Red Hat OpenShift), trustworthy and explainable AI governance (watsonx.governance), production MLOps,
and translating business domain challenges into quantifiable ROI.
1. TECHNICAL COMPETENCIES & ENTERPRISE DATA SCIENCE
Core Machine Learning & Statistical
Modeling
Languages:
Python (pandas, NumPy, SciPy,
scikit-learn), R, SQL, Scala.
Statistical Methods:
Predictive Analytics,
Hypothesis Testing, A/B Testing, Time Series
Forecasting (ARIMA, Prophet), Bayesian
Inference.
Supervised & Unsupervised:
XGBoost,
LightGBM, Random Forests, K-Means
Clustering, PCA, Isolation Forests.
Optimization & Operations Research:
Linear/
Integer Programming, Decision Optimization,
IBM CPLEX.
Generative AI, LLMs & Foundation Models
IBM watsonx Platform:
watsonx.ai,
watsonx.data, watsonx.governance, IBM
Granite Models.
Deep Learning Frameworks:
PyTorch,
TensorFlow, Keras, Hugging Face
Transformers.
GenAI Architecture:
Retrieval-Augmented
Generation (RAG), Fine-Tuning (LoRA/QLoRA),
LangChain, LlamaIndex, Vector Databases
(Milvus, Chroma).
Natural Language Processing:
NLTK, spaCy,
BERT, Sentiment Analysis, Named Entity
Recognition (NER).
Big Data, Data Engineering & Hybrid Cloud
Big Data Frameworks:
Apache Spark
(PySpark), Hadoop, Hive, Presto/Trino, Kafka.
Cloud & Hybrid Infrastructure:
IBM Cloud,
Red Hat OpenShift, AWS, Azure, Docker,
Kubernetes.
Data Warehousing & Lakes:
IBM Db2,
Snowflake, Databricks, Apache Iceberg, Delta
Lake.
Data Integration & ETL:
IBM Cloud Pak for
Data, Airflow, dbt, Feature Stores (Feast).
MLOps, AI Ethics & Enterprise Delivery
MLOps & CI/CD:
MLflow, Kubeflow, Model
Registry, Automated Pipelines, CI/CD for
Machine Learning.
Trustworthy & Responsible AI:
AI Fairness
(AIF360), Explainable AI (SHAP, LIME,
AIX360), Model Monitoring & Drift Detection.
Visualization & Business Analytics:
IBM
Cognos Analytics, Tableau, Power BI, Streamlit,
Plotly.
Methodologies & Governance:
IBM Data
Science Methodology, Agile Data Science,
Model Risk Management (MRM).
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Data Scientist at IBM Resume Skills Blueprint
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