Onboarding plan

Data Scientist Onboarding Plan (30-60-90 Days)

A practical, printable plan to take a new data scientist from first day to full productivity.

Updated By the Career Education Institute Curriculum Team
Data Scientist onboarding at a glance

Week one covers orientation, safety and shadowing. Days 1–30 build vocabulary and supervised practice on core procedures; days 31–60 move to independent work with competency sign-offs; days 61–90 focus on full productivity, cross-training and a credential plan.

Before day one

  • Confirm start date, schedule, pay and supervisor in writing
  • Verify required credentials: Google Advanced Data Analytics Certificate, Microsoft Certified: Azure Data Scientist Associate (as applicable)
  • Prepare equipment and access: python libraries such as pandas and scikit-learn; jupyter notebooks; SQL databases and data warehouses; visualization tools such as Tableau or Power BI
  • Assign a buddy or preceptor
  • Enroll the new hire in Data Scientist training so foundations are covered before or during week one

Week one: orient and protect

  • Workplace tour, emergency procedures and incident reporting
  • Protect personal and sensitive data
  • Follow privacy laws and company policies
  • Check models for bias and fairness
  • Take screen breaks and use ergonomic setups
  • Shadow an experienced data scientist through a full shift
  • Training modules 1–3: role foundations, a day on the job and essential vocabulary (15 terms)

Days 1–30: learn

Goal: perform core tasks safely under supervision.

  • Supervised practice: clean a dataset for analysis
  • Supervised practice: build and evaluate a prediction model
  • Supervised practice: analyze an a/b test
  • Training modules 4–7: duties, step-by-step tasks, tools and safety
  • 30-day check-in: confidence, questions and early wins

Days 31–60: contribute

Goal: handle a regular workload independently.

  • Gather and clean data from many sources: independently
  • Explore data with statistics and visualizations: independently
  • Build predictive and machine learning models: independently
  • Design and analyze experiments: independently
  • Competency sign-offs completed (see checklist below)
  • Training final knowledge check and certificate of completion
  • 60-day check-in

Days 61–90: own

Goal: full productivity and a growth plan.

  • Meet productivity and quality targets for the role
  • Begin cross-training on a secondary task
  • Agree on a credential goal (for example, Google Advanced Data Analytics Certificate)
  • Discuss the career path: Data Analyst → Data Scientist → Senior or Lead Data Scientist
  • 90-day review

Competency checklist

Skill or taskTrainedObservedSigned off
Clean a Dataset for Analysis
Build and Evaluate a Prediction Model
Analyze an A/B Test
Python or R
SQL
Statistics
Machine learning

How to measure onboarding success

  • Time to first independent ticket or deployment
  • First-contact resolution or defect rate
  • Security incidents caused by user or configuration error
  • 90-day retention

How Data Scientist training maps to this plan

  1. Introduction to the Data Scientist Role
  2. A Day on the Job
  3. Essential Data Scientist Vocabulary
  4. Core Duties and Responsibilities
  5. Common Tasks, Step by Step
  6. Tools, Equipment and Technology
  7. Safety, Compliance and Professional Standards
  8. Skills Employers Look For
  9. Launching Your Data Scientist Career
  10. Final Knowledge Check
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