Data Scientist - QuantumBlack

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McKinsey & Company, Inc.

Daerah Khusus Ibukota Jakarta

USD 30,000 - 90,000

Be among the first applicants.

4 days ago

Job description

Your Growth

You will work with other data scientists, data engineers, machine learning engineers, designers, and project managers on interdisciplinary projects, using math, stats, and machine learning to derive structure and knowledge from raw data across various industry sectors.

You are a highly collaborative individual who is capable of laying aside your own agenda, listening to and learning from colleagues, challenging thoughtfully, and prioritizing impact. You search for ways to improve things and work collaboratively with colleagues. You believe in iterative change, experimenting with new approaches, learning and improving to move forward quickly.

Your Impact

Only at McKinsey will you work on real-world, high-impact projects across a variety of industries. You will have the opportunity to collaborate with QB/Labs teams and build complex and innovative ML systems to accelerate our work in AI and help solve business problems at speed and scale.

You will experience the best environment to grow as a technologist and a leader. You will develop a sought-after perspective connecting technology and business value by working on real-life problems across a variety of industries and technical challenges to serve our clients on their changing needs.

You will be surrounded by inspiring individuals as part of diverse and multidisciplinary teams. You will develop a holistic perspective of AI by partnering with the best design, technical, and business talent in the world as your team members.

While we advocate for using the right tech for the right task, we often leverage the following technologies: Python, PySpark, the PyData stack, SQL, Airflow, Databricks, our own open-source data pipelining framework called Kedro, Dask/RAPIDS, container technologies such as Docker and Kubernetes, cloud solutions such as AWS, GCP, and Azure, and more.

As a Data Scientist, you will:

Partner with our clients, from data owners and users to C-level executives, to understand their needs and build impactful analytics solutions.
Contribute to cross-functional problem-solving sessions with your team and deliver presentations to colleagues and clients.
Translate business problems into analytical problems and develop models aimed at solving our clients' and users' problems and ensure they are evaluated with the relevant metrics.
Write highly optimized code to advance our internal Data Science Toolbox.
Add real-world impact to your academic expertise, as you are encouraged to write papers and present at meetings and conferences should you wish.
Take part in R&D projects; attend conferences such as NIPS and ICML as well as data science retrospectives where you will have the opportunity to share and learn from your co-workers.
Work in one of the most advanced data science teams globally.
Work on the frameworks and libraries that our teams of data scientists and data engineers use to progress from data to impact.
Guide global companies through data science solutions to transform their businesses and enhance performance across industries including healthcare, automotive, energy, and elite sport.

Your qualifications and skills

Bachelor's, master's, or PhD level in a discipline such as computer science, machine learning, applied statistics, mathematics, engineering, or artificial intelligence.
2+ years of professional experience in applying machine learning and data mining techniques to real problems with copious amounts of data.
Programming experience (focus on machine learning): SQL and Python’s Data Science stack are a must; good knowledge of at least one big data framework (PySpark, Hive, Hadoop) is a plus; R, SPSS, SAS (nice to have); Software Engineering is a plus.
Ability to prototype statistical analysis and modeling algorithms and apply these algorithms for data-driven solutions to problems in new domains.
Experience deploying technology applied to business problems is a plus.
Knowledge in applying machine learning solutions to real problems with complex and/or large amounts of data.
Knowledge in prompt engineering & GenAI application building (RAG, Agentic systems). Having a basic understanding of Engineering standards, QA/Risk Management.
Willingness to travel.