What you will do:
- Translate customer pain-points into problem statements, architect analytics solution, and engagingly present results and learnings to both technical and non-technical audiences.
- Develop and manage entire end-to-end lifecycle of scoping of data inputs, data cleaning and pre-processing, feature engineering, building models, deploying to production and improving models by iterations
- Present statistically sound model validations to justify model selection and performance
- Build and deploy highly valuable, efficient, scalable advanced analytics models in production systems
- Design and develop sophisticated visualizations and dashboards to explain the actionable insights
- Contribute to the data architecture engineering decisions to support analytics.
- Work closely with project manager and technical leads to provide regular status reporting and support them to refine issues/problem statements and propose/evaluate relevant analytics solutions
- Work in interdisciplinary teams that combine technical, business and data science competencies that deliver work in waterfall or agile software development lifecycle methodologies
- The range of accountability, responsibility and autonomy will depend on your experience and seniority, including:
- Contributing to our internal networks and special interest groups
- Mentoring to upskill peers and juniors
The ideal candidate should possess:
- Possess good communications skills to understand our customers core business objectives and build end-to-end data centric solutions to address them
- Good critical thinking and problem-solving abilities
- Curiosity to ask why and tenacity to find the root causes
- Enthusiasm for implementing machine learning products through extensive experimentation from prototyping to production
- Stay up to date with evolving analytics concepts and data science platforms, tools, and techniques
- Ability to work independently and manage multiple task assignments
- 2 years of experience in advanced analytics delivery / research for full-time applicants or academic exposure for interns applying for this role
- Ability to communicate complex quantitative analysis in a concise and actionable manner
- Proficiency in manipulating and analysing complex, high-volume, high-dimensionality data (structures/unstructured) from varying sources
- Strong knowledge in Feature Selection/Extraction on a variety of data types
- Strong competency in various machine learning techniques (supervised/ unsupervised learning)
- Solid understanding of advanced analytics (Statistics, NLP, Simulation, Optimizations, etc.)
- Expertise in Python/R, Apache Spark (or similar scripting language) coding capability to operationalize data analytics workflows & processes
- Experience in data visualisation tools and libraries such as Tableau, Qlik, Shiny Plotly, ggplot2, etc
- Experience in machine learning model management and deployment tools using containerisation (Docker, Kubernetes)
- Experience in Amazon Web Services, Microsoft Azure, Cloudera, Hadoop, Spark, Storm or related paradigms and associated tools such as Pig, Hive, Mahout
- Experience with DevOps tools in analytics project delivery
- Experience with application/ software development and design
- Exposure in deep learning, and reinforcement learning job experience
- Experience in implementing Graph database analytics
- Knowledge in database modelling and data warehousing concepts
