Have you heard about the IBM Garage? It's a cross-functional team that delivers a unique client co-creation experience to accelerate client transformation. We use Enterprise Design Thinking, our industry-leading IBM Garage Methodology and IBM's multidisciplinary experts in full speed from the start. We design, develop, test, and deliver solutions. Startup speed. Enterprise scale. We apply user-centric approaches to ensure all features add value for the user and achieve desired client impact.
Your Role and Responsibilities
As a Data Scientist working at the IBM Garage, you will be the Subject Matter Expert on Data and AI Statistical models and how they apply to business problems.
Led by a solution architect, you will advise on and help implement models in Machine Learning, Optimization, Neural Networks, and Artificial Intelligence such as Natural Language, and other quantitative approaches.
You will act as a key contributor partnering with clients to understand business problems and propose solutions.
You will deliver meaningful insights and predicts emerging trends to inform business solutions that optimize client value.
You will contribute to co-creation of rapid proofs of concept and minimally viable solutions that demonstrate business value, leading to client investment in strategic solutions.
To be successful in this role you:
- Demonstrate strong business acumen and ability to understand business problems, formulate hypotheses and test conclusions to influence solution design
- Are familiar with data science practices from working in code notebooks, building, validating models, and deploying models via APIs into applications or workflows, and monitoring & retaining models.
- Are able to create machine learning pipelines and train models.
- Are familiar with Data Engineering techniques (gather, prepare, cleanse, and transform client data for analysis and AI automation, including automating data pipelines)
- Leverage a variety of structured and unstructured data sources, analytics, AI tools, and programming languages to derive meaningful data insights (e.g., Python, R, Spark, TensorFlow, Jupyter, etc.)
- Possess relevant industry and/or business domain knowledge such as Finance or Health Care which you will apply to shape solutions
- Work in an Agile fashion, iterating on the solution to react to client feedback and demonstrate rapid progress
- Expand partnerships at all levels of the client organization to identify new opportunities for data science applications
Minimum Salary based on Austrian market ranges: EUR 45.500,- p.a. Overpayment is possible based on the candidate's education and experience.
Required Technical and Professional Expertise
- Graduate degree in computer science, statistics, information systems or another quantitative field
- At least 4 years experience in programming skills in Python, R, Scala or Java (preferred Python)
- At least 4 years as a Data Scientist with a deep understanding of Statistics and Probability
- At least 4 years of experience in the field of machine learning (data ingestion, feature engineering, modelling including ensemble methods, predicting, explaining, deploying and diagnosing over fitting)
- At least 3 years of experience in applying supervised, unsupervised and semi-supervised learning techniques
- At least 2 years of experience in deep learning and neural nets
- At least 2 years in a client facing role (communicating effective with line-of-business end users and being used to lead client discussions)
- Experienced with applications to visualize data using open source tools such as Shiny
- Fluent in German and English language
Preferred Technical and Professional Expertise
- Academic training in a quantitative discipline and/or a specialized degree in data science or analytics
- Understanding of Analytics Life Cycle and / or Crisp DM method and practices
- Experience with RESTful APIs
- Demonstrable experience in private and public Cloud
- Experience in developing and delivering appropriate analytic and/or machine learning models to achieve business goals
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