76 to 100 of 3,579 Agile Jobs

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...

decision gates to continue, pivot, or stop. Lead PoCs and MVPs Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers … outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak. Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production. Technical fluency: able to bridge data science, engineering, security, and business stakeholders ...