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 buyers 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?
Your job is innovating with data, by owning end-to-end data science projects that are transparent, trustworthy, and explainable to stakeholders. Critical for success is to understand the business domain, up and downstream processes and customer needs, being able to assess validity/relevance of results, measure progress and impact. You will be expected to wrangle big data, apply machine learning methods, and to provide meaningful recommendations and actionable insights to key stakeholders.
Develop new machine learning and deep learning models to drive innovation in the marketing domain.
Wrangle data from multiple data sources; synthesize information from seemingly inconsequential data; consult with data owners to build features you deeply understand the meaning of.
Engineer features and help stakeholders understand their meaning.
Train, finetune and explain models.
Ensure that raw model performance translates to business impact.
Document your experiments and production systems
Come up with your own ideas and suggestions for problems to be solved, priorities to be set.
Create ad-hoc reports to help the broader organizations decision-making.
Take part of the in-house ML infrastructure development
What are the expectations for the job?
M.S. in Computer Science, Statistics, Mathematics, Data Science, or related field.
Deep statistical knowledge in classical ML methods: Logistic Regression, Random Forests, Ensemble Methods.
Proficiency in Python, Numpy, Pandas, Scikit-learn, (Jupyter) Notebook, SQL, Git.
Competency in Machine Learning, Deep Learning, Software Engineering and Databases
Knowledge of Neural Nets is a plus.
Experience with Pytorch, PySpark, Jira, and Unix commands.
Deep knowledge of Oracle SQL, PL/SQL, OCI, and other cloud data science tech stacks.
Proficient in Object-Oriented Design Principles
Knowledgeable in system design principles.
What do we offer?
The possibility of personal development
Friendly work environment
Flexible working hours
Birthday day off
How to apply: at booth b13