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Data Scientist

The Data Scientist works on applied data science projects which enable great, personalized customer experiences which drive SVSB’s KPIs of growing our customer base and increasing engagement. The responsibilities include developing production-ready models and algorithms for a given area (recommendations, marketing promotions, onboarding etc.) which maximize customer engagement, working with product & engineering to smoothly deploy these personalized experiences, and measuring the business metric impact of these models through experiments and analysis. This role will require applied experience of machine learning techniques, excellent grasp of statistical knowledge, programming fluency, a strong knack for problem-solving and ability to work cross-functionally with product, engineering and business teams.

In This Role You Will:
  • Collaborate with product & business stakeholders to define potential data science use-cases to drive customer personalization and engagement and develop the corresponding metrics to measure business and customer impact.
  • Ideate, develop and prototype machine learning models and algorithms that intelligently serves personalized experiences to a customer in any of the given areas e.g. in-app recommendations, marketing promotions and onboarding funnels.
  • Own and perform the end to end activities of the machine learning development lifecycle, including data wrangling, feature engineering, training, validation and model tuning, with appropriate documentation of the methodology.
  • Collaborate with product engineering teams to deploy the models into production and ensure they scale well with high volumes of data.
  • Design, run and analyze statistically robust experiments e.g. A/B and multivariate tests to measure impact of product changes to KPI improvement.
  • Continually monitor the efficacy of model performance and iteratively improve them as the business and customers behavior change over time.
  • Perform exploratory analysis using a variety of customer data sources to extract customer behavioral patterns and identify opportunities for growth and new data products.
  • Apply robust and maintainable coding standards along with to ensure high quality outputs to model consumers.
  • Efficiently manage his/her own project queue and communicate ongoing or planned data science products to non-technical stakeholders and senior leadership from time to time.
  • Contribute to the data team knowledge base and overall team growth through mentoring of junior members of the data team and knowledge sharing.

Key Challenges:

  • The role is a multifaceted one which will require applied experience of machine learning/AI techniques, excellent grasp of statistical knowledge, programming fluency, a strong knack for problem-solving and ability to work cross-functionally with product, engineering, and business teams.


Dimension of Impact / Quantitative Data:

  • KPIs/Outcomes Associated to the Role:

    Business Impact

    • # of Active Customers (Acquisition & Retention)
    • # of Orders per Customer (Engagement)
    • Fuel & Store Sales Volume (RM)

    Functional Metrics

    • # of Models Experimented in Production
    • # of Uplift-Positive Initiatives

You’re a great fit if you have:

  • Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, Engineering or other related quantitative fields, with MSc and PHD qualification a strong bonus
  • 3+ years of hands on data science & machine learning experience, with a track record of successful deployments and business impact
  • Experience in B2C e-commerce or loyalty with a mobile app product is preferred
  • Financial services experience is a plus
  • Understanding of the key drivers within the payments/loyalty domain is preferred, but not a requirement

Technical & Functional Skills:

  • Solid theoretical foundations in core data science techniques, supervised or unsupervised e.g. linear and logistic regression, regularization, support vector machines, clustering, dimensionality reduction etc
  • Familiarity with ML frameworks and libraries e.g. Scikit-Learn, Tensorflow, PyTorch
  • Prior experience of implementing consumer product personalisation solutions e.g. recommender systems or algorithm-assisted marketing offer / promotion engines is preferred
  • Experience in deep learning, reinforcement learning, and neural networks is a bonus but not a requirement
  • Strong understanding of inferential statistics, and able to identify causal impact through experimentation procedures (I.e. A/B testing) or causal inference methods
  • Fluency in a scientific computing language (Python, R) and SQL
  • Hands on experience of all stages in the machine learning development lifecycle, including data wrangling, feature engineering, training, validation and model tuning
  • Ability to collaborate with product and data engineers to translate models into production-ready state and push machine learning models to production
  • Familiarity with agile development practices and version control (Git)
  • Familiarity with big data tools and processing frameworks such as Hadoop, Spark, NoSQL datastores
  • Prior experience of working in a cloud infrastructure setting such as AWS, GCP, Azure is preferred

Soft Skills:

  • Ability to work independently and collaboratively with others in the data team and different functional teams e.g. product and engineering
  • Positive team player with strong collaboration skills
  • Able to take ownership and drive initiatives
  • Able to distill and communicate complex technical concepts and solutions at a level appropriate for the intended audience.
  • High energy, self-motivated and able to thrive in a fast-paced environment

Humble and customer-centric

Bonus Qualifications

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