About Us
Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. Our mission is to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively, providing certainty to shape a better tomorrow.
Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare.
About the Role
As an AIML Engineer, your role will involve a blend of analytical translation, problem-solving, client engagement, algorithmic expertise, quantitative mastery, and cross-functional collaboration. You will be instrumental in conceptualising and developing analytical solutions anchored in statistical and machine learning methodologies to address complex business problems.
Responsibilities
- Analytical Translation: Translate complex business problems into sophisticated analytical structures, conceptualising solutions anchored in statistical and machine learning methodologies.
- Problem Solving: Apply technical proficiency in data manipulation, statistical modelling, and machine learning to solve real-world business problems.
- Client Engagement: Establish a deep understanding of clients' business contexts, working closely to unravel intricate challenges and opportunities.
- Algorithmic Expertise: Develop and refine algorithms and models, sculpting them into powerful tools to surmount intricate business challenges.
- Quantitative Mastery: Conduct in-depth quantitative analyses, navigating vast datasets to extract meaningful insights that drive informed decision-making.
- Cross-Functional Collaboration: Collaborate seamlessly with multiple teams, including Consulting and Engineering, fostering relationships with diverse stakeholders to meet deadlines and bring Analytical Solutions to life.
Requirements
- 2-8 years of relevant Data Science Experience, with demonstrated proficiency and hands-on experience navigating data science complexities.
- Good communication skills, both verbal and written.
- Exhibit a fervour for crafting modular, scalable, and bug-free Python code.
- Comfortable in SQL with additional proficiency in office tools like Excel & PowerPoint.
- Experience in production engineering best practices (e.g., Git versioning, Docker).
- Familiarity or experience with working on large data sets and distributed computing (e.g., Hive, Hadoop, Spark).
- Working knowledge of Cloud platforms (e.g., AWS, Azure, GCP).
- Excitement to collaborate with diverse stakeholders across the organisation.
- In-depth understanding of various data science approaches, machine learning algorithms, and statistical methods.
- Hunger to learn new technologies and embrace the change.
- Proficiency in foundational concepts and algorithms in machine learning, encompassing regression and classification techniques, and a keen awareness of their assumptions, strengths, and limitations.
- Must-Have Skills: Regression/Classification/Optimization/ Python Proficiency in these key skills is crucial to thriving in this role.