Data Science Lead (SG) - Innovation Lab

Our client equips the financial community with access to an open platform that uncovers opportunity and catalyzes change. Customers can access solutions for every challenge, including a breadth of applications, tools, content - all supported by human expertise. They facilitate the connections that propel people and organizations to find new possibilities to move forward.

As Data Science Lead, you will suggest new uses of data to hypothesize ways of transforming the financial industry and translate end user needs to requirements by employing methods of design thinking. You will also apply data science practices to construct prototypes and MVPs to formulate new ideas. This will be a chance for you to enhance your skill set through mentoring and collaborating with our highly adept professionals

Along with a Singapore team of data scientists, engineers and UX/UI designers, as well as a global network of sister labs around the world, your responsibilities will be to:

  • Work with large, diverse sets of data, including big data and public cloud technology, to construct minimal viable products for our client’s customers

  • Identify, organize and manage projects across multiple geographies and timezones

  • Initiate proposals with technologists and business to drive innovation from conception to production, using relevant success metrics

  • Spearhead and contribute to a highly talented team of data scientists, simultaneously acting as a manager

  • Be a project leader (from a data science perspective) to eliminate obstacles and offer creative solutions

  • Facilitate internal and external stakeholder communications at senior levels to uncover problems and use cases, whilst translating business impact of current and past projects

  • Establish domain expertise in financial markets and in our client’s products and data

  • Build relationships in the big tech, fintech and academic community by attending meetups, workshops and conferences to incorporate burgeoning technology and techniques into our client’s business strategy

Required Skills:

  • More than 7 years of industry experience in data science with solid experience with Python or R

  • Proficiency in tools/libraries such as scikit-learn, numpy, pandas and jupyter

  • Familiarity with Relational, NoSQL & Graph database, such as PostgreSQL, MongoDB, Elasticsearch or Neo4J

  • Experience using at least several of the algorithm techniques, such as Clustering, Classification, Regression, Decision Trees, Neural Networks, Random Forest, Support Vector Machines, Hidden Markov Models, Latent Dirichlet Allocation and more

  • Unsupervised, semi-supervised and supervised experience building large datasets for machine learning and deep learning models

  • Demonstrated leadership skills in data science teams, with proven ability to convey expectations and objectives, and align to business goals that involve an inclusive, knowledge sharing and feedback-driven culture

  • Experience in rapidly producing minimum viable products with a focus on results and ability to prioritize impactful deliverables

  • Able to track down issues involving complex data quality and data integration, evaluate various algorithmic approaches, and to analyze data to solve problems

  • Excellent interpersonal skills and proven capability to manage multiple projects in a collaborative and dynamic environment

  • Results-oriented and positive attitude to new skills and technologies

  • Highly confident in communicating with both internal and external senior business and technical leaders

  • Domain knowledge in financial services such as quantitative finance, Electronic Trading, Risk Modelling or Financial Engineering

Desired Skills:

  • Master’s Degree or PhD in a relevant technical field including computer science, applied mathematics or a related discipline such as physics or chemistry

  • Experience with Cloud (e.g. AWS, Google Cloud Platform, Microsoft Azure) to carry out large scale calculations

  • Familiarity with language frameworks/tools such as Scala, Spark, Stan, Gremlin, Hadoop and Flink

  • Experience with Spark ML/MLLib

  • Experience with deep learning frameworks such as TensorFlow, Keras, Caffe, Torch/PyTorch, Theano and mxnet

  • Ability to handle large and distributed datasets on software such as Spark, Hadoop, Hive, Pig or Storm

  • Experience in machine learning/deep learning pipelines, data ingestion, feature engineering and selection, model training, validation and deploying large-scale machine learning/deep learning models in the cloud environment

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