Data Science Tech Lead/AI/ML Lead-Remote

Tekaccel Inc.; Texas; Remote; USD 160,000 - 230,000; Full time

Job summary

Tekaccel Inc. is seeking a Data Science Tech Lead/ AI/ML Lead to own ML initiatives end to end in a remote role.

You will collaborate with clinical product, operations, and business stakeholders to translate goals into technical problems and deliver measurable improvements in therapy persistence and intervention timing. The role requires leading the full ML lifecycle, designing solutions for time-to-event/survival framing, and ensuring model explainability in clinical workflows.

Qualifications

• Experience leading ML projects end to end, from framing to deployment.
• Ability to translate clinical goals into technical problem statements.
• Proven track record delivering production ML systems with measurable impact.
• Strong collaboration with product, operations and business teams.

Responsibilities

• Own the initiative end to end from framing to shipping a solution.
• Lead ML lifecycle: data sourcing, feature engineering, modeling, evaluation, deployment.
• Ensure explainability and clinician trust in predictions.
• Drive delivery, mentor team, and manage stakeholders.

Skills

Data science leadership

Project leadership

Communication with stakeholders

Problem framing

Tools

Python

ML/DL frameworks

AWS

Databricks

Spark

Model Explainability

Job description

• Own the initiative end to end - from framing the problem to shipping a solution that measurably improves therapy persistence and intervention timing.
• Partner with clinical product, operations, and business stakeholders to identify requirements, translate ambiguous clinical goals into a concrete technical problem statement, and define what success looks like.
• Refine the modeling target beyond "predict any adverse event" toward the outcomes that matter - therapy discontinuation, serious/clinically meaningful events, and intervention timing.
• Design the solution
• Lead solution design for the full ML lifecycle: data sourcing, feature engineering, modeling approach (including time-to-event / survival framing), evaluation, explainability, and deployment into clinical workflows.
• Make sound build decisions across per-product modeling, concomitant-therapy effects, class imbalance, noisy labels, and point-in-time correctness.
• Design for explainability and clinician trust - every prediction should be interpretable and actionable.
• Drive delivery & lead the team
• Break the work into iterative, demonstrable deliverables - prove value early (e.g., descriptive insights and a risk report) before full workflow integration.
• Define milestones and roadmap, run daily standups, remove blockers, and hold the team and yourself accountable to committed dates.
• Build the solution hands-on alongside data scientists and engineers, set the technical quality bar, and mentor/develop the team.
• Manage stakeholders & navigate the organization
• Manage expectations proactively - communicate what's feasible now vs. later, surface risk early, and renegotiate scope rather than silently slipping.
• Navigate cross-team and political boundaries delicately, including dependencies on data-owning teams you don't control; build trust, escal
• Communicate exceptionally well to both technical and non-technical audiences, including delivering honest assessments when the data or timeline demands it.

Role: Data Science Tech Lead/ AI/ML Lead

Location: Remote

What You’ll Do

• Own the problem & the outcome
• Own the initiative end to end - from framing the problem to shipping a solution that measurably improves therapy persistence and intervention timing.
• Partner with clinical product, operations, and business stakeholders to identify requirements, translate ambiguous clinical goals into a concrete technical problem statement, and define what success looks like.
• Refine the modeling target beyond "predict any adverse event" toward the outcomes that matter - therapy discontinuation, serious/clinically meaningful events, and intervention timing.
• Design the solution
• Lead solution design for the full ML lifecycle: data sourcing, feature engineering, modeling approach (including time-to-event / survival framing), evaluation, explainability, and deployment into clinical workflows.
• Make sound build decisions across per-product modeling, concomitant-therapy effects, class imbalance, noisy labels, and point-in-time correctness.
• Design for explainability and clinician trust - every prediction should be interpretable and actionable.
• Drive delivery & lead the team
• Break the work into iterative, demonstrable deliverables - prove value early (e.g., descriptive insights and a risk report) before full workflow integration.
• Define milestones and roadmap, run daily standups, remove blockers, and hold the team and yourself accountable to committed dates.
• Build the solution hands-on alongside data scientists and engineers, set the technical quality bar, and mentor/develop the team.
• Manage stakeholders & navigate the organization
• Manage expectations proactively - communicate what's feasible now vs. later, surface risk early, and renegotiate scope rather than silently slipping.
• Navigate cross-team and political boundaries delicately, including dependencies on data-owning teams you don't control; build trust, escal
• Communicate exceptionally well to both technical and non-technical audiences, including delivering honest assessments when the data or timeline demands it.

Required Qualifications

• 7+ years of data science and machine learning experience, delivering models that reached production or driven real business decisions.
• 3+ years leading machine learning or data science projects end to end - from problem framing through deployment.
• 3+ years working directly with business stakeholders and product owners: managing expectations, owning delivery, and driving a high-visibility workstream under tight deadlines.
• Significant solution-design experience for building machine learning systems, not just individual models.
• Strong hands-on expertise in Python and the modern ML/DL ecosystem.
• Deep, practical understanding of machine learning and deep learning - able to choose the right approach and reason about tradeoffs, evaluation, and failure modes.
• Experience with AWS and Databricks for building and deploying data/ML solutions at scale.
• Experience working with healthcare data (and awareness of the associated data-quality, privacy, and governance realities).
• Demonstrated leadership presence: self-motivated, driven to deliver results, and able to earn the confidence of both technical teams and senior stakeholders.

Preferred Qualifications

• Experience in pharmacy, specialty pharmacy, or clinical/patient-outcomes domains, with working familiarity of the relevant datasets (therapy, dosing, adverse events, discontinuation, claims).
• Familiarity with time-to-event / survival analysis and its application to intervention-timing problems.
• Experience deploying models into clinical or operational workflows with human-in-the-loop decisioning and measurable outcome validation (e.g., controlled rollouts).
• Exposure to Generative AI / agentic approaches and a pragmatic view of where they fit in a regulated setting.
• Experience with data governance, PHI/HIPAA constraints, and model documentation in a regulated environment.
• Familiarity with MLOps practices: feature stores, model monitoring, and reproducible pipelines.

Core Skills & Technologies

• Python
• Machine Learning
• Deep Learning
• AI / GenAI
• AWS
• Databricks
• Spark
• Survival Analysis
• Model Explainability
• MLOps
• Healthcare Data
• Solution Design