
FinOpsly is an AI automation platform that unifies cloud, data, and AI operations into a frictionless, accountable system of action. Built for modern enterprises, FinOpsly connects cost, performance, and governance signals to deliver real-time clarity and automated execution across complex, multi-environment technology landscapes. By turning insights into action, FinOpsly helps organizations eliminate waste, increase efficiency, and operate with confidence at scale.

FinOpsly is an AI automation platform that unifies cloud, data, and AI operations into a frictionless, accountable system of action. Built for modern enterprises, FinOpsly connects cost, performance, and governance signals to deliver real-time clarity and automated execution across complex, multi-environment technology landscapes. By turning insights into action, FinOpsly helps organizations eliminate waste, increase efficiency, and operate with confidence at scale.
What they do: AI-first FinOps platform for real-time cost visibility, governance and automated remediation across cloud, data, and AI stacks
Key product modules: Costix, ASK FI, Radar, TotalView TCO (pre-deployment estimates, chargebacks, anomaly detection, policy-driven automation)
Integrations: AWS, Azure, GCP, Databricks, Snowflake
HQ & team size: Cincinnati; ~29 employees
Recent funding: $1.92M seed round reported (Oct 15, 2024) led by Hyde Park Venture Partners
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Cloud cost management, FinOps, cloud/data/AI cost governance and automation
FinOps / Cloud Cost Management
$1.92M
Dealroom reports seed round with participation from Narayana Surabhi and Cintrifuse Capital
Crunchbase lists a Pre-Seed round and names Hyde Park Venture Partners and Catalystrix Ventures as investors; amounts obfuscated
“Backed by institutional and angel investors including Hyde Park Venture Partners, Catalystrix Ventures, Cintrifuse; investor logos shown on company About page”
FinOpsly i s an AI-native Value-Control™ platform for cloud (AWS, AZ, GCP), data (Snowflake, Databricks), and AI economics, built to help enterprises move beyond passive cost visibility to active, outcome-driven control. The platform unifies technology spend across cloud infrastructure (AWS, Azure, GCP), data platforms (Snowflake, Databricks), and AI workloads into a single system of action—combining planning, optimization, automation, and financial operations.
We’re hiring a hands-on Data Scientist with deep application-level expertise in Snowflake or Databricks — someone who understands how workloads behave, how platform services scale, and how architectural choices impact cost and latency.
This is not a generic ML role. It is applied optimization science for modern data platforms.
What You’ll Work On
· Analyze query history, warehouse/cluster utilization, and workload telemetry
· Build anomaly detection models for cost spikes and performance degradation
· Develop right-sizing and optimization recommendation engines
· Translate platform signals into prescriptive, explainable insights
· Partner with engineering to embed intelligence into customer-facing modules
· Quantify measurable savings and performance gains
· Build an advanced optimization and intelligence engine to reduce data platform costs, improve performance, and detect anomalies in real time.
What We’re Looking For
· 5+ years in Data Science, Applied ML, or Performance Engineering
· Deep expertise in Snowflake (warehouses, clustering, query plans, credit usage)
or
Databricks (Spark optimization, cluster sizing, Delta Lake, DBU usage)
· Strong SQL + Python (pandas / PySpark / ML libraries)
· Experience with time-series modeling and anomaly detection
· Passion for optimization, automation, and measurable impact
Why This Role Matters
You will help enterprises move beyond reporting into intelligent, automated value control — where platform usage is continuously optimized, and every dollar of data spend is aligned to performance and business outcomes.
If you thrive at the intersection of data science, distributed systems, and cloud economics, this role is built for you.