One of our leading clients has a requirement for a Data Science Manager to manage their high-performing team and work closely with the software engineering teams
· Manage a team to drive measurable value.
· Conceptualise, analyse, build, test, and implement models that utilize various and diverse data sets to solve problems related to the employment and recruiting industries.
· Build and implement machine learning models, testing frameworks, and visualisations to make timely decisions and influence the product roadmap.
· Proactively research data and identify opportunities in the data to drive the product roadmap.
· Evaluate data's big picture to solve business problems rather than focusing on metrics alone.
· Understand human behavior and know what to look for in the data.
· Communicate concisely and persuasively with engineers and product marketers.
· Help identify ways to improve data quality and analysis.
· Experience managing data science/analytics teams.
· Significant prior success as a Data, Product, or Decision Scientist working on challenging problems at scale.
· 6+ years of industry experience, with expertise in Data Science, Machine Learning.
· Proficient in Python, Pandas, SQL.
· Bachelors / Masters degree / PhD in Computer Science, Statistics, Mathematics, Artificial Intelligence, Quantitative Methods, Cognitive Science, or a closely related quantitative field.
· Expertise in data mining, statistical modeling, and machine learning.
· Passion for optimizing Business performance, Client Lifecycle and Client Experience with data.
· Full stack experience in data collection, aggregation, analysis, visualization, productization, and monitoring of data science products.
· Deep expertise in applied machine learning, preferably in digital advertising, auctions, or bidding systems.
· Strong track record of delivering high business value in a fast-paced dynamic environment.
· Experience in client lifecycle management (e.g. retention; optimization) will be an advantage.
· Experience running controlled online experiments and taking data-driven decisions for improving machine learning-based products. Preferably in non-independent settings, like marketplaces.