Senior Data Science Manager

AvaHR; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Ubiety Technologies, Inc. is seeking a Senior Data Science Manager to lead vision, architecture, and execution of data science initiatives. You will oversee a team of data scientists, guide model development, and align technical output with strategic company goals.

This is a player-coach role balancing leadership with hands-on technical work. The role requires strong bias for action, ability to set direction, and comfort solving complex problems at scale.

Qualifications

• Advanced degree or equivalent practical experience in a quantitative field.
• 7+ years in data science, ML, or related discipline.
• 3+ years leading technical teams and driving impact in production systems.
• Strong ability to translate business problems into data science solutions.

Responsibilities

• Lead and mentor a team of data scientists.
• Own the architecture and execution of data science initiatives.
• Develop, validate, deploy, and monitor ML models and analytics systems.
• Collaborate with executives, product, and engineering to drive business outcomes.

Skills

Machine learning

Python

SQL

Leadership

Communication

Education

MS in Data Science/CS/Statistics

BS in a quantitative field

Tools

AWS

GCP

BigQuery

Airflow

TensorFlow/PyTorch

Job description

At Ubiety Technologies, Inc we are redefining physical security. We develop innovative device detection and RF signaling products that protect homes, businesses, and communities. As part of our fast-moving, highly collaborative startup team, you'll work alongside an exceptional team of data scientists, engineers, and creative designers to invent and deliver transformative products from inception to production.

We are proud of our culture of innovation, speed, ownership, and accountability.We value agility and embrace ambiguity so we can move quickly, iterate effectively, and outperform traditional approaches. Every employee is an equity owner in the company and we expect our team members to think and act like an owner would: understand what matters most, prioritize accordingly, identify problems before they become blockers, and take responsibility for driving solutions forward. As a startup, focus, ingenuity, and speed combine to create our competitive advantage.

The Senior Data Science Manager leads the vision, architecture, and execution of Ubiety’s data science initiatives. They oversee the data science team and drive the development of advanced machine learning models and predictive analytics while aligning technical output with company-wide strategic objectives. The right candidate will be both a stellar people leader and technical contributor.

This is a player-coach role. The right candidate is equally comfortable setting technical direction, developing talent, partnering with executives, reviewing architecture, digging into data, writing or reviewing code, and stepping directly into a difficult technical problem when that's what the business needs.

We are looking for someone with a strong bias for action who knows how to distinguish what is important from what is merely interesting. You should be energized by complex technical problems and the opportunity to build capabilities that have not existed before!

If you'remotivated by building, experimenting, solving difficult problems, and seeing your work translate into measurable outcomes, we want to hear from you!

Responsibilities

As a key technical leader, this role will:

• Lead, mentor, and develop a high-performing team of data scientists while fostering a culture of technical excellence, accountability, curiosity, and continuous improvement.

• Serve as a hands-on technical leader who can move fluidly between strategy and execution, stepping directly into modeling, analysis, architecture, experimentation, or troubleshooting when needed.

• Establish the strategic roadmap and measurable milestones for data science initiatives, ensuring resources are concentrated on the opportunities with the greatest potential business and product impact.

• Translate broad or ambiguous business problems into clear hypotheses, technical approaches, experiments, and measurable outcomes.

• Partner closely with executive leadership, product, engineering, and other teams to identify and prioritize high-impact opportunities for machine learning, predictive analytics, and data-driven decision-making.

• Lead the development, validation, deployment, and continuous improvement of scalable machine learning models and analytical systems.

• Make thoughtful technical and product decisions with incomplete information, balancing speed, quality, scalability, and business impact.

• Build an experimentation culture that encourages calculated risk-taking, rapid learning, and iteration rather than waiting for perfect information.

• Continuously look for ways to accomplish more with available resources through better architecture, automation, tooling, prioritization, and creative problem-solving.

• Establish clear success measures before work begins and use data to determine whether initiatives are delivering the intended impact.

• Identify risks, dependencies, and blockers early and take ownership of driving them toward resolution.

• Drive hiring and onboarding efforts, identifying talent and capability gaps and scaling the team thoughtfully as business needs evolve.

• Champion strong practices in data governance, experimental design, model validation, observability, documentation, and code quality.

• Ensure technical solutions ultimately translate into usable products, better decisions, improved customer outcomes, or measurable business value.

Technical Skills and Abilities

• Deep expertise in machine learning, statistical modeling, predictive analytics, and experimental design.

• Strong proficiency in Python, including tools such as NumPy, Pandas, Scikit-learn, PyTorch and/or TensorFlow.

• Advanced SQL skills and demonstrated ability to work directly with large and complex datasets.

• Experience designing, deploying, monitoring, and improving machine learning models in production environments.

• Proven experience with cloud-native data and machine learning infrastructure such as AWS or GCP and technologies such as BigQuery, Snowflake, and Airflow.

• Strong understanding of data engineering principles, streaming architectures, data quality, and their impact on machine learning pipelines.

• Ability to evaluate emerging tools, techniques, and technologies quickly and determine where experimentation is warranted.

• Exceptional ability to synthesize complex technical findings and communicate them clearly to both technical and non-technical stakeholders.

Requirements

• BS or MS degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience.

• 7+ years of progressive experience in data science, machine learning, applied statistics, or a related discipline.

• 3+ years of experience leading, managing, or developing technical teams.

• Demonstrated record of taking complex data science or machine learning initiatives from concept and experimentation through production and measurable business impact.

• Strong experience hiring, developing, coaching, and retaining high-performing technical talent.

• Demonstrated ability to operate effectively in environments where priorities evolve, information is incomplete, and resources are constrained.

• Strong ownership mentality with a record of proactively identifying problems, driving decisions, and following through to results.

• Ability to prioritize aggressively, focus teams on the highest-value work, and say no or not yet to lower-impact opportunities.

• Comfortable challenging assumptions, testing new approaches, learning from unsuccessful experiments, and adjusting quickly.

• Low-ego, hands-on leadership style with a willingness to do the work required to help the team succeed.

Preferred

• Working knowledge of technologies such as Kafka, Flink, Spark Structured Streaming, or other real-time and streaming data architectures.

• Experience building data science or machine learning capabilities within a startup, high-growth technology company, or similarly resource-constrained environment.

• Experience working with device, RF, IoT, telecommunications, location, behavioral, or other high-volume signal data is a plus.

Key Success Metrics

Business Impact

Data science initiatives produce measurable improvements in company, customer, product, or operational outcomes.

Speed to Value

The team moves effectively from hypothesis to experiment to production, delivering meaningful learning and measurable progress quickly.

Prioritization & Focus

Team resources are consistently concentrated on the highest-impact opportunities, with clear tradeoffs and minimal effort spent on work that does not advance company objectives.

Production Performance

Machine learning models and analytical systems are reliable, scalable, observable, and continuously improved based on real-world performance.

Team Performance & Development

Data scientists operate with increasing autonomy, technical depth, ownership, and effectiveness as a result of strong coaching and leadership.

Cross-Functional Impact

Data science operates as an effective partner to product, engineering, and company leadership and translates technical capabilities into actionable business and product value.

Innovation & Learning Velocity

The team consistently tests new ideas, technologies, and approaches, quickly distinguishing promising opportunities from those that should be abandoned or deprioritized.

Ownership & Execution

Risks and blockers are surfaced early, commitments are followed through, and the team consistently converts decisions into action and action into measurable results.

Ubiety Technologies, Inc is an Equal Opportunity Employer. We celebrate diversity in all forms and are committed to maintaining a discrimination-free workplace that treats applicants and employees with dignity and respect. Our employment process is conducted without regard to race, color, religion, nationality or ethnic background, sex, pregnancy, sexual orientation, gender identity or expression, age, disability, protected veteran status, genetic information, or other attributes protected by state, federal, and local law.