Are you a Machine Learning Engineer looking to make a big impact at the intersection of AI, Data Science, and Business Strategy? Tiger Analytics is a rapidly growing advanced analytics consulting firm, and we’re looking for talented individuals to join our world-class team. We work with Fortune 500 companies to help them generate business value from their data, and we need your expertise to continue driving innovation and growth.
Our global leadership in analytics consulting has been recognized by Forrester and Gartner, and now we need top-tier talent to help take our solutions to the next level. Are you ready to take on the challenge and work on cutting-edge Machine Learning systems in a fast-paced, dynamic environment? Keep reading to learn more!
What You’ll Do:
- Develop and Deploy ML Solutions: Create scalable, high-performance Machine Learning systems that have a real-world impact. You'll be responsible for the deployment, execution, validation, monitoring, and continuous improvement of data science solutions.
- Build Reusable Pipelines: Design and build production data pipelines for machine learning models, ensuring they’re highly reusable and scalable for future use.
- Write Production-Quality Code: You’ll write robust, production-quality code and libraries that can be easily deployed, packaged, and run in containers.
- Collaborate and Innovate: Work closely with Data Engineers, Data Scientists, and business partners to ensure that ML models and data pipelines are running smoothly, and drive strategy with your analytical skills.
- Troubleshoot & Optimize: Tackle production ML model issues head-on—retrain, revalidate, and optimize solutions to keep everything running at peak performance.
What We’re Looking For:
- 5+ years of experience in Machine Learning Engineering, with a strong background in Python, Spark, Hadoop, and Docker.
- Bachelor’s degree in Computer Science or a related field (or equivalent experience).
- Deep knowledge of ML frameworks like Scikit-learn, TensorFlow, Keras, and familiarity with MLflow, Airflow, and Kubernetes.
- Experience with cloud environments like AWS SageMaker and knowledge of Big Data technologies, working with structured and unstructured data.
- A strong ability to collaborate with cross-functional teams, communicate clearly, and manage the infrastructure and data pipelines required for ML production.
- A mindset focused on continuous integration, model evaluation, and experimental design.
Additional Skills that Will Make You Stand Out:
- Expertise in programming languages such as Python and SQL.
- Proficiency in statistical tools, relational databases, and other data management tools.
- Experience in designing and implementing scalable machine learning systems that can be maintained in production for the long term.
Why Tiger Analytics?At Tiger Analytics, we offer a dynamic, entrepreneurial environment where you will have significant career development opportunities. As part of our growing team, you’ll be empowered with individual responsibility, the chance to work on high-impact projects, and an opportunity to contribute to the success of global organizations.
You’ll be surrounded by top-tier talent and will have the chance to develop your skills while working on some of the most challenging and exciting problems in the field of analytics and AI.
Benefits:
- Competitive salary and performance bonuses.
- Health, dental, and vision insurance.
- Work-life balance with flexible working arrangements.
- Professional development and learning opportunities.
- A culture of innovation, collaboration, and growth.
Ready to Make an Impact?If you're passionate about Machine Learning and want to help businesses unlock the full potential of their data, Tiger Analytics is the place for you. We’re excited to meet driven, talented engineers who are ready to take their careers to new heights.
