Not just an employment relationship!
Also with a contract of assignment!
About the Company:
The Marketing Technology (MarTech) team is part of the Customer Experience (CX) product development organization responsible for building tools and processes allowing marketers to craft frictionless buyer’s journeys for our partner’s vast portfolio of infrastructure products and business applications. The MarTech team maintains the infrastructure, integration and processes aiding marketers from data capture all the way to ML applications and everything in between.
About the team:
The Data Science team is part of MarTech and consists of data engineers, data scientists and ML engineers. Their job is to deliver data products and ML applications that improve productivity, deliver insights, and allow marketing and business development teams to engage their audiences with timely and relevant messaging. They prototype and build their solutions for their marketing teams with the objective of incorporating them into CX portfolio of products.
Their stakeholders and collaborators include teams responsible for marketing their products, infrastructure, and instrumentation as well as other data science teams. They also work with a broad array of marketing technology and data vendors to improve their data and capabilities.
What they Do?
They work with large volume, transaction level data (including clickstream, demographics, firmographics, marketing responses, purchase and business transactions, and service consumption) and operate on a global scale, leveraging their Cloud Infrastructure services. As a full-stack data team they explore, process, cleanse, standardize data to calculate features for their ML efforts, maintain/customize their own tooling and infrastructure to advance MLOps initiatives and strive for engineering excellence across the board. They also provide low level ML tooling for other data science teams and consult on technology/process integration. Part of their work is focused on building and maintaining data foundations around identity/entity resolution, taxonomy curation, data augmentation, and system integration – all of which requires strong engineering and data skills as well as understanding of the B2B enterprise technology domain. They use their infrastructure and data foundation elements to build data products and ML applications that their customers, the marketers interface with directly. Their applications include account and contact level targeting/segmentation, ranking/prioritization, experimentation, measurement, attribution, personalization, content recommendation, and forecasting.
If you are:
A collaborative individual who prioritizes progress over perfection, with a mentality of “never stop learning”. Someone who has developed the ability to actively listen — both to colleagues and customers – as well as to lead. You are an individual that builds trust over the course of challenging projects and is dependable to persist through unforeseen obstacles. You are a seasoned professional ready to pick up more and more about the B2B enterprise software domain with a drive to deliver results.
You are int the right place!
What tasks await the future colleague?
You will perform full-stack ML development (including DevOps) to design ML systems and modeling solutions that are observable, trustworthy, and explainable to stakeholders. A priority for this role is to increase productivity of the team by improving the ML model building, deployment and monitoring capabilities. You will design end-to-end automated machine learning systems, tools, and solutions that consume both structured and unstructured data, and to ensure that these solutions yield business value to stakeholders. This is an opportunity to apply machine learning workflows in high impact high visibility scenarios and solve scalability and performance challenges that require state-of-the-art cloud technologies.
Develop ML systems and DevOps tools to improve the performance of ML solutions in the marketing domain.
Build and maintain production-grade ML tools, data pipelines, and statistical models
Establish scalable, efficient, automated processes for large scale data analysis, model development, model validation and model implementation
Build monitoring tools for deployed models to ensure their performance and reliability.
Work on the life cycle of model development including: data collection, data cleaning, feature engineering, model training, tuning, evaluation, deployment, and monitoring.
Provide technical support for the team and document APIs.
What are the expectations for the job?
M.S. in Computer Science, Statistics, Mathematics, Data Science, or related field.
Proficient in Machine Learning, Software Engineering, DevOps, and Oracle SQL
Experienced in writing and shipping production quality code for the deployment of ML pipelines.
Experienced in building testable and extensible software that is reliably deployed with CI/CD processes.
Track record of writing clean, documented code that is object-oriented, encapsulated, modularized, and demonstrates an understanding the SOLID Principles of OOP.
Proficient in system design principles and optimizing for Time and Space complexity.
Proficient in Python, R, and Java or C#.
Proficient in Pythonpackages: Numpy, Pytorch, Pandas, Scikit-learn, (Jupyter) Notebook, SQL, Git, Jira
Deep knowledge of Oracle SQL, PL/SQL, OCI, and other cloud data science tech stacks.
Instinctively understand the stages of an ML Pipeline and prioritize which stages should be addressed first based on the results of a particular model
Critical thinking: the ability to analyze problems and solutions from multiple angles.
Portability: work on projects in various domains and of varying complexity to untangle and optimize legacy code, whether it’s in SQL, Python or Java.
What do we offer?
The possibility of personal development
Friendly work environment
Flexible working hours
Birthday day off
How to apply: at booth b13