Position: Technical Project Manager (with Data Science background)
Company: Luxoft
Category: Project Manager
Experience required: 5y
English level: fluent

Responsibilities:
As a TPM on our team you will understand detailed functional requirements, collaborate with product managers, software design engineers, Machine Learning Engineers and AI Platform architects and administer these in the Product backlog. The successful individual will work closely with multiple teams, across multiple functional and partners to understand the business needs and requirements and help define the product specifications. Bring your strong leadership and communication skills, customer centricity, agile project management experience, creative problem solving, data analysis skills, and experience facilitating and implementing change to our team.
Mandatory Skills Description:
What we are looking for in a successful applicant:
• Support and collaborate with our data scientist team on lodging sort
• Report complex data science status and developments to partners and executives in clear straightforward language, incorporating references and explanations as needed for audience comprehension
• Facilitate conception, adoption and regular updating of project objectives and metrics
• Maintain project roadmaps and link them to team, division and corporate strategies and objectives
• Drive communication and collaboration across multiple groups with different priorities
• Partner with product and engineering teams to conceive, architect, develop and deliver user-centric intelligent products
• Identify interdependencies between concurrent projects and adjust workflows accordingly
• Demonstrated ability to independently gather and turn business requirements into technical requirements and drive discussions among multiple technical teams to scope and schedule effort
• Handle partner concerns and expectations
• Identify risks and creatively resolve blocking issues with the team. Drive resolution on cross-team issues, scope changes, prioritization collisions, resource constraints, and other impediments
• Manage data science lifecycle development following data science-adapted Agile Kanban methodology
Nice-to-Have Skills:
Technological landscape that we use:
• Machine-Learning/Data engineering: TensorFlow, Databricks, Spark, SQL, Tableau
• Cloud: AWS Cloud (S3, EC2), and other in-house tooling built upon AWS infrastructure (notably for our machine learning inference platform)
• Languages used: Java/Scala/Python
• Engineering backend: Git/Jenkins/Spinnaker
• Management: Jira, Confluence