Senior Data Scientist

**Role Purpose**:
We are looking for a seasoned Data Scientist who has real-world machine learning experience. You have experience owning end-to-end machine learning models, from requirements and ideation to model development, deployment, and monitoring. Experience in creating models for Credit Risk or Fraud is a huge plus. You feel equally comfortable performing data mining and machine learning tasks on your laptop as you are in large compute environments. You take pride in owning and maintaining the technical work environment. You enjoy telling the data story and are capable of communicating deep analytical insights to less experienced members of the R&D team and our customers. Lastly, you have been looking for an opportunity where you can use your passion and skills for a greater good against financial crime.

**Example Responsibilities**:

- Collaborate with the R&D team to build a distributed cloud-based SaaS solution for transaction monitoring
- Leading a team of data scientists from a technical perspective.
- Solving the fraud problem for clients of the largest companies in the industry: HSBC, United, Monzo, AirBNB and many others.
- Build scalable machine learning models, algorithms, and analytics on large financial datasets to detect suspicious activity.
- Feel comfortable presenting to senior audiences and articulating cost/benefit tradeoffs.
- Build complex data sets from multiple data sources, both internally and externally.
- Research new techniques, algorithms, and best practices within the industry.
- Explore, evaluate, and integrate new data pipelines.
- Participate in client interactions.

**Experience and Qualifications**:

- Advanced degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field.
- Proven and relevant industry experience
- Business driven and result-oriented individual.
- In-depth knowledge of Statistics, Probability, and Machine learning.
- Supervised and unsupervised statistical techniques such as regression, time series analysis, clustering, dimensionality reduction.
- Machine learning algorithms and concepts such as Random Forest, Boosting, SVMs, neural networks.
- Exposure to NLP models and techniques such as sentiment analysis, word embeddings, Latent Dirichlet Allocation (LDA) is a plus.
- Fluent in Python, including data science tools such as scikit-learn, PyTorch or TensorFlow, pandas.
- Experience with big data technologies such as Map/Reduce, Hadoop, Hive, and Spark.
- Experience of being a tech lead is a big plus.
- Passionate about writing reusable quality code.
- Effective communication & collaboration skills.

**Great to Have Experience and Qualifications**:

- Experience working with data on Neo4j, HDFS, Cassandra, ElasticSearch, MongoDB, MySQL.
- Built backend systems using Java and Python.
- Worked in a financial services context related to risk management.
- Experience with trading systems and banking environments.

Jumios London office is based in Shoreditch, an easy walk from Old St, Liverpool St or Moorgate. It's the leading global sales office at Jumio and has several people from other head office functions, it's growing rapidly. It's a friendly and social culture with the obligatory food, pool, darts and office dog (when we're lucky). In a recent culture survey these attributes were rated particularly highly in Jumios London office.

**Jumio Values**:
IDEAL: Integrity, Diversity, Empowerment, Accountable, Leading Innovation

**Equal Opportunities**:
**About Jumio**:
Jumio is a B2B technology company dedicated to eradicating online identity fraud, money laundering and other financial crimes to help make the internet safer. We leverage AI, biometrics, machine learning, liveness detection and automation to create solutions that are trusted by leading brands worldwide and respected by industry thought leaders.

Jumio is the leading provider of online identity verification, eKYC and AML solutions. With a global footprint, we're expanding the team to meet strong client demand across a range of industries including Financial Services, Travel, Sharing Economy, Fintech, Gaming, and others.

**Applicant Data Privacy