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Role: Senior Machine Learning Engineer

Job Location: London, UK & Nuremburg, Germany

Job Duration: Contract, 6 months

Industry: Market Research

Job Description:

  • Design, develop and implement data science and machine learning models in proof-of-concepts and prototypes
  • Develop, optimize, standardize and implement data science and machine learning solutions at scale in data pipelines and distributed systems (eg Hadoop/Spark/Kubernetes ecosystem)
  • Engineer at scale using service-oriented architecture, containerized applications and functions as a service, especially in cloud service environments (ie IaaS, PaaS, SaaS, FaaS)
  • Optimize data science and machine learning models using high performance computing (eg GPGPU) and Real Time techniques (eg messaging/streaming services, reactive programming)
  • Represent machine learning and data science expertise at workshops and conferences

We look for a profile with complementary skills, such as:

  • Expert skills with regard to code performance optimization and scalability (eg, parallelization, sharding, scattering, gathering etc.)
  • Solid understanding of service-oriented architectures (eg microservices) & distributed systems (eg Hadoop, Kubernetes).
  • Solid knowledge of cloud computing environments and tooling (AWS, Azure).
  • Hands-on experience with scalable machine learning frameworks and general data science tooling required.
  • Hands-on experience with continuous integration and big data environments required.
  • Hands-on experience with Docker & Kubernetes required.
  • Background in a statistical or mathematical field, ideally with a computational element, such as Physics, Data Science, Computer Science is a must
  • Experience in development of custom machine learning models from prototyping to scalable implementations would be a strong plus.

Our toolset/ecosystem:

  • OS: Linux (Ubuntu, Debian, CoreOS)
  • Programming languages: Java, Scala, C#, C
  • Analytical languages: Python, Spark (Java, Scala, Pyspark, SparkR), R
  • ML-Frameworks: H2O, TF, Spark MLlib, Theano, Torch, Caffe, MXNet,
  • HPC: CUDA, Numba, C/C
  • Presentation: Jupyter, Dash, Shiny, Vaadin
  • Backend: Flask, Dash, Eve, Spring, Spring Boot
  • Big Data: Hadoop, Hive, Spark, Accumulo, Nifi
  • Messaging/Streaming: Kafka, Flink
  • DevOps: Docker, Kubernetes, Bitbucket, Bamboo, Nexus, ELK, Prometheus, Ansible, Terraform

Role: Senior Data Scientist

Job Location: Nuremberg, Germany

Job Duration: Contract, 6 months

Client Industry: Market Research

Job description:

  • Design and develop analytical approaches based on product concepts, going all the way from feasibility checks to proof-of-concepts to prototypes to implementation in end-to-end solutions.
  • Manage and coordinate Data Science projects, ie defining user stories and specific tasks working closely with Product Owners and Product Managers.
  • Closely collaborate with machine learning engineers/software engineers on creating data-driven end-to-end solutions for marketers.
  • Provide guidance and leadership to non-Senior Data Scientists in your workstream.
  • Present Data Science capabilities in client-facing situations, at conferences and in workshops.
  • Actively share Data Science knowledge within Technology and Data network.

We look for a profile with complementary skills, such as:

  • Expert knowledge of Python programming, including Spark.
  • Solid skills with regard to working in Big Data environments (eg, Cloudera Hadoop, Hortonworks Data Platform, AWS EMR, Map/Reduce, Hive).
  • Expert knowledge of the constantly evolving Data Science ecosystem and its frameworks (eg, Hadoop, Spark MLlib, H2O, Tensorflow, Torch, Theano).
  • Basic skills with regard to performance optimization and scalability (eg, parallelization, code optimization, containerization, function as a service).
  • Expert skills with regard to database handling, in particular SQL, NoSQL.
  • Expert statistical modelling skills.
  • Expert knowledge of optimization and/or machine learning algorithms and/or deep learning algorithms.
  • Solid understanding of Big Data Architectures.
  • PhD or Master degree that reflects strong modelling, statistics and IT skills.
  • 5 years of professional experience in quantitative data analysis/data science or related areas
  • Used to work in agile Scrum teams, ideally experienced with SAFe.