← AI / Machine Learning Engineering — P2

Goal templates — AI / Machine Learning Engineering — P2

Data Science & Analytics · AI / Machine Learning Engineering · P2 — Developing Professional

These are canon-derived frames, not advice: every line is either verbatim JobFrame canon text or a fixed template wrapping it. ⟨target⟩ / ⟨baseline⟩ / ⟨date⟩ are placeholders for the manager to fill in. Nothing here is generated by AI — rows are omitted, never invented, when the canon lacks the underlying field.

SMART goals

One row per canon core output / responsibility this level owns.

JFM responsibility (P2)

Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance.

Specific
Deliver: "Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance."
Measurable
Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable
Scoped to this level's jfm complexity/problem-solving rubric: "Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts."
Relevant
Advances the Data Science & Analytics · AI / Machine Learning Engineering mandate for a P2 — Developing Professional.
Time-bound
⟨date⟩

JFM responsibility (P2)

Trains models and evaluates model performance on defined segments of a larger project with senior oversight.

Specific
Deliver: "Trains models and evaluates model performance on defined segments of a larger project with senior oversight."
Measurable
Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable
Scoped to this level's jfm complexity/problem-solving rubric: "Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts."
Relevant
Advances the Data Science & Analytics · AI / Machine Learning Engineering mandate for a P2 — Developing Professional.
Time-bound
⟨date⟩

JFM responsibility (P2)

Codes with guidance and documents results, shipping small features with support.

Specific
Deliver: "Codes with guidance and documents results, shipping small features with support."
Measurable
Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable
Scoped to this level's jfm complexity/problem-solving rubric: "Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts."
Relevant
Advances the Data Science & Analytics · AI / Machine Learning Engineering mandate for a P2 — Developing Professional.
Time-bound
⟨date⟩

JFM responsibility (P2)

Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks.

Specific
Deliver: "Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks."
Measurable
Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable
Scoped to this level's jfm complexity/problem-solving rubric: "Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts."
Relevant
Advances the Data Science & Analytics · AI / Machine Learning Engineering mandate for a P2 — Developing Professional.
Time-bound
⟨date⟩

JFM responsibility (P2)

Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers.

Specific
Deliver: "Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers."
Measurable
Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable
Scoped to this level's jfm complexity/problem-solving rubric: "Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts."
Relevant
Advances the Data Science & Analytics · AI / Machine Learning Engineering mandate for a P2 — Developing Professional.
Time-bound
⟨date⟩
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1. Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance.  [source: JFM responsibility (P2)]
   Specific:    Deliver: "Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts."
   Relevant:    Advances the Data Science & Analytics · AI / Machine Learning Engineering mandate for a P2 — Developing Professional.
   Time-bound:  ⟨date⟩

2. Trains models and evaluates model performance on defined segments of a larger project with senior oversight.  [source: JFM responsibility (P2)]
   Specific:    Deliver: "Trains models and evaluates model performance on defined segments of a larger project with senior oversight."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts."
   Relevant:    Advances the Data Science & Analytics · AI / Machine Learning Engineering mandate for a P2 — Developing Professional.
   Time-bound:  ⟨date⟩

3. Codes with guidance and documents results, shipping small features with support.  [source: JFM responsibility (P2)]
   Specific:    Deliver: "Codes with guidance and documents results, shipping small features with support."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts."
   Relevant:    Advances the Data Science & Analytics · AI / Machine Learning Engineering mandate for a P2 — Developing Professional.
   Time-bound:  ⟨date⟩

4. Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks.  [source: JFM responsibility (P2)]
   Specific:    Deliver: "Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts."
   Relevant:    Advances the Data Science & Analytics · AI / Machine Learning Engineering mandate for a P2 — Developing Professional.
   Time-bound:  ⟨date⟩

5. Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers.  [source: JFM responsibility (P2)]
   Specific:    Deliver: "Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts."
   Relevant:    Advances the Data Science & Analytics · AI / Machine Learning Engineering mandate for a P2 — Developing Professional.
   Time-bound:  ⟨date⟩

OKRs

Objectives from this level's core outputs; key results only where a real dimension or capability backs them.

JFM responsibility (P2)

Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance.

  • From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance."
  • Evidence at this level's scope bar: "Defined deliverables / small features" — ⟨target⟩ by ⟨date⟩

JFM responsibility (P2)

Trains models and evaluates model performance on defined segments of a larger project with senior oversight.

  • From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Trains models and evaluates model performance on defined segments of a larger project with senior oversight."
  • Evidence at this level's autonomy bar: "General supervision; reviewed at milestones" — ⟨target⟩ by ⟨date⟩

JFM responsibility (P2)

Codes with guidance and documents results, shipping small features with support.

  • From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Codes with guidance and documents results, shipping small features with support."
  • Evidence at this level's complexity bar: "Some non-routine problems; applies established patterns" — ⟨target⟩ by ⟨date⟩

JFM responsibility (P2)

Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks.

  • From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks."
  • Evidence at this level's impact bar: "Own and immediate-team deliverables" — ⟨target⟩ by ⟨date⟩

JFM responsibility (P2)

Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers.

  • From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers."
  • Evidence at this level's decision rights bar: "Routine technical choices within guidance" — ⟨target⟩ by ⟨date⟩
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Objective 1: Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance.  [source: JFM responsibility (P2)]
  KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance."
  KR2. Evidence at this level's scope bar: "Defined deliverables / small features" — ⟨target⟩ by ⟨date⟩

Objective 2: Trains models and evaluates model performance on defined segments of a larger project with senior oversight.  [source: JFM responsibility (P2)]
  KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Trains models and evaluates model performance on defined segments of a larger project with senior oversight."
  KR2. Evidence at this level's autonomy bar: "General supervision; reviewed at milestones" — ⟨target⟩ by ⟨date⟩

Objective 3: Codes with guidance and documents results, shipping small features with support.  [source: JFM responsibility (P2)]
  KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Codes with guidance and documents results, shipping small features with support."
  KR2. Evidence at this level's complexity bar: "Some non-routine problems; applies established patterns" — ⟨target⟩ by ⟨date⟩

Objective 4: Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks.  [source: JFM responsibility (P2)]
  KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks."
  KR2. Evidence at this level's impact bar: "Own and immediate-team deliverables" — ⟨target⟩ by ⟨date⟩

Objective 5: Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers.  [source: JFM responsibility (P2)]
  KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers."
  KR2. Evidence at this level's decision rights bar: "Routine technical choices within guidance" — ⟨target⟩ by ⟨date⟩

MBO areas

Key result areas from this level's responsibilities, each with a standard grounded in the canon leveling rubric where one exists.

AreaStandardTargetDue
Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance.Consistent with this level's jfm knowledge-application rubric: "Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance."⟨target⟩⟨date⟩
Trains models and evaluates model performance on defined segments of a larger project with senior oversight.Consistent with this level's jfm knowledge-application rubric: "Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance."⟨target⟩⟨date⟩
Codes with guidance and documents results, shipping small features with support.Consistent with this level's jfm knowledge-application rubric: "Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance."⟨target⟩⟨date⟩
Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks.Consistent with this level's jfm knowledge-application rubric: "Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance."⟨target⟩⟨date⟩
Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers.Consistent with this level's jfm knowledge-application rubric: "Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance."⟨target⟩⟨date⟩
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1. Area: Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance.  [source: JFM responsibility (P2) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance."
   Target:   ⟨target⟩   Due: ⟨date⟩

2. Area: Trains models and evaluates model performance on defined segments of a larger project with senior oversight.  [source: JFM responsibility (P2) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance."
   Target:   ⟨target⟩   Due: ⟨date⟩

3. Area: Codes with guidance and documents results, shipping small features with support.  [source: JFM responsibility (P2) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance."
   Target:   ⟨target⟩   Due: ⟨date⟩

4. Area: Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks.  [source: JFM responsibility (P2) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance."
   Target:   ⟨target⟩   Due: ⟨date⟩

5. Area: Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers.  [source: JFM responsibility (P2) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance."
   Target:   ⟨target⟩   Due: ⟨date⟩

Scorecard

Only perspectives with real canon backing are shown — no Financial or Customer perspective, since nothing in the canon grounds business-financial or customer measures for a role alone.

Internal process

  • "Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance."→ ⟨target⟩ by ⟨date⟩
  • "Trains models and evaluates model performance on defined segments of a larger project with senior oversight."→ ⟨target⟩ by ⟨date⟩
  • "Codes with guidance and documents results, shipping small features with support."→ ⟨target⟩ by ⟨date⟩
  • "Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks."→ ⟨target⟩ by ⟨date⟩
  • "Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers."→ ⟨target⟩ by ⟨date⟩

Role calibration

  • Meets the scope bar: "Defined deliverables / small features"→ ⟨target⟩ by ⟨date⟩
  • Meets the autonomy bar: "General supervision; reviewed at milestones"→ ⟨target⟩ by ⟨date⟩
  • Meets the complexity bar: "Some non-routine problems; applies established patterns"→ ⟨target⟩ by ⟨date⟩
  • Meets the impact bar: "Own and immediate-team deliverables"→ ⟨target⟩ by ⟨date⟩
  • Meets the decision rights bar: "Routine technical choices within guidance"→ ⟨target⟩ by ⟨date⟩
  • Meets the leadership bar: "May guide interns"→ ⟨target⟩ by ⟨date⟩
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Internal process
  - "Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (P2)]
  - "Trains models and evaluates model performance on defined segments of a larger project with senior oversight."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (P2)]
  - "Codes with guidance and documents results, shipping small features with support."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (P2)]
  - "Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (P2)]
  - "Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (P2)]

Role calibration
  - Meets the scope bar: "Defined deliverables / small features"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Scope)]
  - Meets the autonomy bar: "General supervision; reviewed at milestones"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Autonomy)]
  - Meets the complexity bar: "Some non-routine problems; applies established patterns"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Complexity)]
  - Meets the impact bar: "Own and immediate-team deliverables"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Impact)]
  - Meets the decision rights bar: "Routine technical choices within guidance"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Decision rights)]
  - Meets the leadership bar: "May guide interns"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Leadership)]