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Tree Pilot Jobs in Missouri (NOW HIRING)

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Tree Pilot information

What is the difference between Tree Pilot vs Arborist?

AspectTree PilotArborist
CertificationsTypically requires safety and equipment operation certificationsRequires ISA Certified Arborist credential
Work EnvironmentOperates equipment, often in forestry or utility settingsWorks directly with trees, pruning, and planting in urban or rural areas
Industry UsageCommon in utility companies, forestry, and land managementCommon in landscaping, tree care companies, and municipal services

Tree Pilots primarily focus on operating equipment and supporting forestry or utility tasks, often requiring safety certifications. Arborists specialize in tree care, pruning, and health assessments, usually holding ISA certification. While both roles work outdoors and in related industries, Tree Pilots tend to focus on equipment operation, whereas Arborists focus on tree health and maintenance.

How much does a tree pilot make?

A tree pilot typically earns between $40,000 and $70,000 annually, depending on experience, certifications, and location. The role involves operating aerial lifts and climbing equipment, often requiring safety training and physical fitness.

What are popular job titles related to Tree Pilot jobs in Missouri?

For Tree Pilot jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Tree Pilot jobs in Missouri look for?

The top searched job categories for Tree Pilot jobs in Missouri are:

What cities in Missouri are hiring for Tree Pilot jobs?

Cities in Missouri with the most Tree Pilot job openings:

Tree Pilot jobs near you

Senior Predictive Liability Analytics Lead

California, MO • On-site

Jobtailor
See more jobs at Jobtailorexternal

$140 - $220/hr

Other

Posted 6 days ago

Job description

Responsibilities
  • Join BSRM as our hands‑on Senior Predictive Liability Analytics Lead.
  • You’ll build next‑gen short‑term models of policyholder behavior starting with annuity surrenders, withdrawals, and utilization.
  • Collaborate with other stakeholders such as financial planning, ALM, pricing, valuation and capital as appropriate.
  • This is a technical leadership role (not initially a people‑manager or strategy role).
  • You’ll do the math, write the code, build models, and mentor by example.
  • Design predictive and semi‑structural models with a short‑term focus (performance focused on fitting the next 18‑36 months) on long‑horizon behavior (surrenders, partial withdrawals, lapse, rider utilization) using GLMs/GAMs, survival & hazard models (Cox, discrete‑time, competing risks), tree ensembles (Boost/LightGBM/CatBoost), and deep learning choosing the most appropriate tool depending on the business problem.
  • Leverage unstructured data (contract text, correspondence, customer relationship notes, call transcripts) via NLP/transformer embeddings, RAG pipelines, and LLM‑assisted document parsing to create novel behavioral features within guardrails.
  • Pilot generative‑AI (foundation models) for feature extraction/summarization; use genetic/evolutionary algorithms for feature selection, architecture search, or synthetic cohort generation when appropriate.
  • Make models scenario‑aware: incorporate drivers like credited rate, market rate spreads, moneyness, surrender charge state, distribution channel effects; calibrate elasticity to economic conditions documented in industry studies.
  • Translate model outputs into curves/driver functions consumable by projection engines (e.g., Moody’s AXIS, Aon Pathwise, Prophet, RAFM, or internal models); generate reproducible, versioned results tables.
  • Share models with valuation/projection/ALM teams so behavior sensitivities can be considered alongside assumptions that normally flow through cash‑flow projections, LDTI assumption updates, RBC/CTE stresses, and hedge effectiveness studies.
  • Partner with valuation and pricing to reconcile actual vs. expected and attribute earnings/variance to behavior; document the “model story” and explainability for governance.
  • Build training/scoring pipelines in Python/SQL on Databricks/Spark/Snowflake/AWS; track experiments with MLflow/DVC, version in Git, package with containers, and serve via batch/API.
  • Stand up dashboards for calibration, drift, stability, and bias; set retraining schedules, fallback models, rollback criteria, and automated alerts.
Requirements
  • Master’s/PhD in Statistics, Data Science/ML, Applied Math, Computer Science, or Actuarial Science; FSA/ASA a plus (or equivalent domain depth).
  • Certifications in ML/AI (nice to have).
  • 7+ years building production predictive models; insurance/annuity or long‑duration liability exposure preferred.
  • Practical wins in behavior modeling (surrender/utilization/lapse) and integration.Comfortable spanning structured + unstructured data and bridging to projection engines.
  • Clear, concise communicator; strong documentation habits; bias to ship and iterate.
  • Mentors by example; sets standards for code quality, reproducibility, and testing.
  • Balances accuracy, interpretability, and operational simplicity under governance.
  • Collaborate within a highly matrixed organization.
  • Python (pandas, NumPy, scikit‑learn, XGBoost/LightGBM, PyTorch/TensorFlow), SQL, R.
  • NLP/LLM: transformers/embeddings, RAG, prompt engineering; genetic/evolutionary search for features/hyper‑params.
  • Databricks/Spark, Snowflake, AWS/Azure; MLflow, model registries, CI/CD; Tableau/Power BI for monitoring & storytelling.
  • Working familiarity with Actuarial platform (AXIS/Prophet/RAFM/etc.) integration patterns (assumption tables, mapping layers).
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