Data Scientist (Consultant/Senior Consultant)

About the role

As a Data Scientist, you will be identifying opportunities for data exploitation through in depth conversations with business stakeholders. You will scope out data science and AI projects/products with clear deliverables that are actionable and impactful and execute them in close iteration with the stakeholders. 

 This role requires an individual with strong data analysis, problem solving, and communication skills, who can engage stakeholders on their business use cases, identify appropriate data science techniques in each case, and deliver sound solutions that seek to influence actionable outcomes. 

Key responsibilities

Facilitate discussions with stakeholders to understand their business challenges, sharpen the business use cases and translate them into data science projects/products.

Perform data cleaning, pre-processing, feature engineering, and build data science models/products to address the use case.

Present findings, solicit feedback and prioritise refinements to the analysis in close iteration with stakeholders while managing overall project timeline.

Communicate the data insights and business impact of the projects/products in a clear and compelling narrative, supported with impactful visuals, to influence key decision makers.

Operationalise data solutions (e.g. models, analytical processing workflows integrating AI models, dashboard, etc.), including deployment of models and data processing pipelines for ModelOps as required, and working with product engineering teams to integrate the data solutions into digital products to serve users

Essential requirements:

A Bachelor's Degree or higher in Data Science, Computer Science, Statistics, Economics, Quantitative Social Science, or related disciplines. We will also factor in relevant certifications (e.g., Coursera).

At least 2 years of relevant experience, in data science, marketing analytics and/or adtech.

Proficiency in one or more data science domains – feature engineering, statistics, machine learning, optimisation techniques;

Proficiency in one or more programming languages and libraries commonly used for data science development (e.g. Python, R);

Proficiency in one or more programming languages and libraries commonly used in wrangling large datasets (e.g. SQL, Spark);

Experience developing and deploying data science solutions for production systems, including knowledge of DevOps and ModelOps principles, processes, and tools (e.g. GitLab, etc.); and

Experience working directly with users to understand business requirements, frame business problems as data science problems, and iteratively execute data science solutions to meet business needs.

Preferred requirements:

Proficiency in developing production-grade model deployment and monitoring pipelines, and optimizing model performance for production systems;

Experience or domain knowledge in marketing and advertising analytics;

Experience working in cross-disciplinary agile teams;

Experience working with cloud services, with preference for Amazon Web Services; and

Experience working with commercial integrated data infrastructure and processing solutions, with preference for Databricks.

Work Arrangements

Hybrid working model: 3 days in office (Wednesday and Thursday, with the third day flexible at the personnel's discretion) as per division policy

Required to attend office meetings even if they fall on work-from-home days (Monday/Tuesday)

Working hours: 8:30 AM - 6:00 PM

Other arrangements may be discussed and are subject to division approval