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. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide 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. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence.

We are a Great Place to Work-Certified™ (2022-24), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine

· Bachelor’s or master’s degree in computer science, Engineering, Data Science, or a related field.

· Tool Expertise - Azure DEVOps / Jira

· At least 0-1 years of experience in Product Development

· Programming experience – At least 2-3 years as an API or Web Developer

· Technology Experience: JavaScript / Web Technologies / React

· Experience in Internet facing Web Products

· Overall 6+ years of experience in developing and managing product projects

· Experience working with data scientists, engineers, and business stakeholders in an Agile environment.

· Knowledge of data science, machine learning, or AI concepts is a significant plus.

1.Project Management Tools & Methodologies:

2. Risk & Quality Management:

Experience managing project risks, dependencies, and constraints in data science projects.

o Stakeholder & Communication:

o Cloud Computing & General Concepts:

Familiarity with cloud platforms like AWS, GCP, or Azure for data science workflows.

Familiarity with data science techniques such as machine learning, statistical modelling, Gen AI, NLP and data mining.

 Prioritization of business use case post understanding of business use cases where DS could be leveraged.

Familiarity with domain – e.g., Retail / Pharma / BFS / Telecom

Understanding of QA Concepts and how to leverage best practices throughout the course of project lifecycle

· Good to have:

o Data Science/Analytics/Data Infrastructure Expertise:

Understanding of data pipelines, ETL processes, and data wrangling techniques.

Understanding or Proficiency in SQL and data manipulation using Python/Pyspark or R.

 Familiarity of data storage systems, big data technologies (e.g., Hadoop, Spark), and distributed computing.

o Data Visualization & Reporting:

Experience with data visualization tools (e.g., Tableau, Power BI, D3.js).

Experience to handle and manage discussion between technical and non-technical stakeholders

Ensure best in class project delivery and governance processes for the project –

· Project Planning, Execution & Coordination:

o Understand the scope of the work and develop the story points by creating WBS (Work Breakdown Structure)

o Coordinate with cross-functional teams (data scientists, engineers, analysts, and business stakeholders) to ensure smooth execution.

o Track the team velocity and assign the work to balance the workload within team.

o Monitor the output and raise the risk in case of any delay / technical gap within team members

o Gather requirement and articulate in the design / wireframe

o Establish and Govern Sign-off Mechanism for key milestones

o Establish Change Management process and maintain the Change Request Log.

o Should be able to drive the discussion on Scope Creep and set the prioritization framework.

o Focus on resource utilization and maintain to govern the schedule and effort variance.

· Stakeholder Management:

o Serve as the primary point of contact between stakeholders, clients, and the data science team.

o Gather and translate business requirements into high-level data science deliverables.

o Communicate progress, results, and risks to stakeholders and management regularly.

o Identify potential opportunity for business growth

· Team Management:

o Lead and mentor a multidisciplinary team of data scientists, ML engineers, and business analysts.

o Foster collaboration between business, IT, and data science teams to solve complex problems.

o Identify and resolve project issues, bottlenecks, and resource constraints.

· Risk Management:

o Identify project risks and implement mitigation strategies.

o Ensuring client regulatory guidelines are adhered wherever applicable

· Project Execution:

o Oversee the entire project lifecycle, ensuring timely delivery of insights, models, and dashboards.

o Implement Agile or Scrum methodologies and conduct ceremonies to ensure iterative and continuous delivery.

o Ensure quality of the deliverables is maintained throughout the project lifecycle

· Reporting and Documentation:

o Create and maintain project documentation, including status reports, timelines, and resource allocation plans.

o Ensure that project milestones are documented and that deliverables are tracked and signed off by stakeholders.

o Post project closure, documenting case studies for reference in future within or outside account

· Continuous Improvement:

o Gather feedback from stakeholders and clients for project improvements.

o Analyse past project outcomes to refine project management methodologies for future data science initiatives.