Ecosystem Data Scientist
-
- Sales
- Professional
Ecosystem Data Scientist
-
- Sales
- Professional
Ecosystem Engineering Data Scientists contribute to the co-creation of Proofs of Value (PoV) and Minimal Viable Product that help the partner demonstrate business value, leading to enabling and embedding of strategic IBM AI technologies (i.e., Watson Libraries, Watson APIs, and IBM applications) in partner solutions. Data Scientists will engage in solving business problems in a wide array of industries around the globe using IBM AI technologies. Data Scientists cover all stages of the data model lifecycle from Data Engineering to Model Building, Machine Learning Operations and AI Governance to create a trusted end-to-end AI solution
Your Role and Responsibilities
As a Data Scientists in the Ecosystem Engineering Blue Partner Lab team , you will support the design, creation, implementation and optimization of data solutions in collaboration with partner engineers and IBM Subject Matter Experts (SMEs). These solutions will drive increased value – for many of the biggest and most complex partner applications in Manufacturing, Energy and Utilities etc. You will work with a team of engineers with expertise in industry-leading data platforms and technologies to implement these solutions.
To succeed, you will become a trusted collaborator with developers and data scientists as we move partner technologies and offerings to IBM software solutions across multi-cloud platforms. In addition, you will have an opportunity to consult and provide guidance to partners as they integrate their products, services, and offerings to IBM software solutions across multi-cloud platforms.
This role will interface with Sales, Product, Ecosystem, Marketing, Consulting, Research, and other IBM delivery organizations, and business development leaders to ensure successful outcomes. Candidates must be comfortable working in a dynamic and rapidly evolving agile work environment.
This is an opportunity for you to help IBM and external partners succeed in growing their hybrid cloud presence.
Required Technical and Professional Expertise
- 4+ years experience in the area of data engineering (Python or similar)
- 1+ years of experience in Big Data technologies (Hadoop, Spark, MapReduce, Apache Airflow)
- 1+ years of experience in ETL, Data Lake and Data Warehousing (e.g. InfoSphere, Db2, Delta Lake, Databricks, Microsoft SQL Data Warehouse)
- 1+ years of experience in container storage and Software Defined Storage (SDS) on Kubernetes
- Experience in Data Security (e.g. TLS/SSL, Certificate and Key Management, encryption/decryption, TPM 2.0, TEE/TEP)
- Experience in multiple database management systems (e.g. Db2, Oracle, SQL Server, Postgres, MongoDB, Informix)
- Experience in Data and AI offerings on public clouds (AWS, Azure, Google, etc.)
- Understanding of different cloud storage offerings, RDBMS, Object Storage, Block Storage, File Storage, HDFS, etc.
- Understanding of data science open source technologies; including Python, Jupyter notebooks, and TensorFlow
- Ability to understand and learn new technologies quickly
- Must exhibit excellent written and verbal English communication skills
Preferred Technical and Professional Expertise
Skills
- Experience in IBM Cloud Pak for Data (CP4D) 3.5 or 4.0
- Experience in Enterprise Asset Management (EAM), e.g. Maximo, SAP, Infor, ABB Ability, Aveva, Oracle EAM
- Experience in Data Fabric and Data Virtualization
- Experience in Watson Knowledge Catalog (WKC), IBM Analytics Engine, Watson Studio
- Experience in REST APIs, OpenAPI Initiative, design, creation, and securing APIs (e.g. Apigee, Mulesoft, IBM API Connect, IBM DataPower)
- Experience in Data Storage Performance Tuning
- Big Data Certifications on Cloudera/Hortonworks/AWS/GCP/Azure
- Experience with deep learning models using Keras
- Experience using Kubernetes and Docker
- Experience with IBM software solutions across multi-cloud platforms
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Key Job Details
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