Applied Data Scientist

Key Skills

Amazon Elastic Compute Cloud-EC2, Amazon Simple Storage Service (S3), Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, Jupyter Notebook, Machine learning techniques, matplotlib, Pandas, Python Programming, PyTorch, R Programming, Scikit-learn, SQL, Statistics, TensorFlow

Job Description

In this role, you will:

• Analyze diverse datasets to identify trends and insights that drive business decisions.
• Collaborate with cross-functional teams to translate complex data into actionable strategies and develop predictive models using Python and/or R.
• Refine visualizations and present findings to stakeholders, ensuring clarity and impact.
• Create real-time machine learning models that leverage massive datasets to solve customer challenges and deliver scalable solutions.
• Turn analytical product ideas into reality, from brainstorming and prototyping to scaling in production.
• Perform hands-on analysis, build machine learning pipelines, and evaluate the performance of solutions in real-world scenarios.
• Work closely with engineering to develop and scale effective solutions for production.
• Identify and define new business challenges in Amazon competitive intelligence, focusing on market trends, traffic, and advertising analytics.
• Maintain smooth operations of machine learning systems, ensuring they deliver millions of predictions daily.

You will excel in this role, if you have:

This isn’t your first rodeo!

To effectively perform this role and achieve the key performance objectives, relevant experience in data science and analytics is essential. A strong background in developing and optimizing predictive models, particularly in sales estimation and product discovery, will enable the individual to identify trends and generate actionable insights. Experience with data manipulation and statistical analysis tools, such as Python, R and SQL, is crucial for analyzing complex datasets. Additionally, familiarity with machine learning techniques and frameworks will be beneficial for building innovative models that enhance product offerings. Prior involvement in cross-functional collaborations will provide the necessary skills to communicate insights clearly and work effectively with diverse teams.

• You’ve got at least 5 years of experience in the industry (or similar) and hold a Master’s or PhD.
• You’re a pro at machine learning best practices and have a solid grasp of statistics.
• You’ve got hands-on experience creating and deploying algorithms and models to production, collaborating closely with machine learning engineers.
• You know how to turn customer needs into data-driven solutions.
• You’re familiar with the software development lifecycle and have worked with version control, CI/CD, and different environments.

Technical Skills:

• Programming Languages: Proficiency in Python and R for data analysis and model development.
• Proficiency with ML libraries: Scikit-learn, Tensorflow, Pytorch, Pandas,
• Numpy, Scipy, Dplyr, Matplotlib, Seaborn
• Data Manipulation: Experience with SQL for querying databases and handling large datasets.
• Machine Learning: Proficiency with machine learning frameworks.
• Statistical Analysis: Strong understanding of statistical methods and techniques.
• Ability to create insightful visualizations
• Experience with Git for version control and collaboration on code.
• Understanding of experimental design and A/B testing methodologies for product optimization.
• Ability to work with APIs for data retrieval and integration of external data sources.

Interest in Amazon. You have a deep curiosity about data and the Amazon domain.

Resilient & Adaptable. You’re resilient and adaptable, comfortable handling ambiguity, making trade-offs, and tweaking your plans as you learn.

Bonus:

• You’ve got experience with other AWS services like EC2 and CloudWatch.
• Familiarity with Spark, SageMaker, and S3.
• You’re also comfortable using Jupyter notebooks.

The behavioural skills to thrive in this role:

• Adaptability: Embrace change and be ready to pivot quickly in response to new data, market trends, or shifting priorities.
• Team Player: Thrive in a fast-paced, collaborative environment, working closely with cross-functional teams to make things happen.
• Curiosity: Always ask questions and seek out new tools, methods, or approaches that can push us forward. A mindset of continuous learning and growth is key.
• Problem Solver: See challenges as opportunities. Tackle obstacles head-on with creative, impactful solutions.
• Detail-Oriented: Pay attention to the small stuff. Your precision in data analysis and model development ensures reliability and drives confidence in decision-making.
• Clear Communicator: Whether you're talking shop with engineers or explaining insights to non-technical stakeholders, you're able to break down complex ideas and deliver them in a way that resonates and inspires action.

Jungle Principles:

• Customer: Focus on building solutions that deliver meaningful value to our users, shaping products that address their needs and improve their experience.
• Decide: Make informed, data-driven decisions quickly, using analysis and insights to steer development and optimize solutions effectively.
• Clarity: Communicate findings and complex data insights in a clear, understandable manner to both technical and non-technical stakeholders.
• Scrappy: Embrace creativity and resourcefulness when solving problems, deploying solutions rapidly and iterating often to drive progress.
• Excellence: Strive for continuous improvement in model development, ensuring high accuracy, reliability, and cutting-edge performance in all deliverables.

Note: The one constant in our evolving, dynamic environment is change. Needless to say, you’ll never get bored ;) The responsibilities listed above are our needs today but we are keeping future needs & opportunities in mind when hiring for this position. We value adaptability and a growth mindset in team members. Although it’s not promised, the successful candidate should be prepared for the possibility that their role may evolve over time as our business strategies and priorities change.