diff --git a/openstef/pipeline/optimize_hyperparameters.py b/openstef/pipeline/optimize_hyperparameters.py
index 6bce42bd6..90f5786b5 100644
--- a/openstef/pipeline/optimize_hyperparameters.py
+++ b/openstef/pipeline/optimize_hyperparameters.py
@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: MPL-2.0
 import os
-from typing import Any
+from typing import Any, Union
 
 import optuna
 import pandas as pd
@@ -22,7 +22,7 @@
 from openstef.model.regressors.regressor import OpenstfRegressor
 from openstef.model.serializer import MLflowSerializer
 from openstef.pipeline.train_model import (
-    DEFAULT_TRAIN_HORIZONS,
+    DEFAULT_TRAIN_HORIZONS_HOURS,
     train_model_pipeline_core,
 )
 from openstef.validation import validation
@@ -43,7 +43,6 @@ def optimize_hyperparameters_pipeline(
     input_data: pd.DataFrame,
     mlflow_tracking_uri: str,
     artifact_folder: str,
-    horizons: list[float] = DEFAULT_TRAIN_HORIZONS,
     n_trials: int = N_TRIALS,
 ) -> dict:
     """Optimize hyperparameters pipeline.
@@ -65,6 +64,12 @@ def optimize_hyperparameters_pipeline(
         Optimized hyperparameters.
 
     """
+    if pj.train_horizons_minutes is None:
+        horizons = DEFAULT_TRAIN_HORIZONS_HOURS
+    else:
+        horizons = [
+            horizon_minutes / 60 for horizon_minutes in pj.train_horizons_minutes
+        ]
     (
         best_model,
         model_specs,
@@ -97,7 +102,7 @@ def optimize_hyperparameters_pipeline(
 def optimize_hyperparameters_pipeline_core(
     pj: PredictionJobDataClass,
     input_data: pd.DataFrame,
-    horizons: list[float] = DEFAULT_TRAIN_HORIZONS,
+    horizons: Union[list[float], str] = DEFAULT_TRAIN_HORIZONS_HOURS,
     n_trials: int = N_TRIALS,
 ) -> tuple[
     OpenstfRegressor, ModelSpecificationDataClass, Report, dict, int, dict[str, Any]
@@ -109,7 +114,7 @@ def optimize_hyperparameters_pipeline_core(
     Args:
         pj: Prediction job
         input_data: Raw training input data
-        horizons: horizons for feature engineering.
+        horizons: horizons for feature engineering in hours.
         n_trials: The number of trials. Defaults to N_TRIALS.
 
     Raises:
@@ -124,9 +129,6 @@ def optimize_hyperparameters_pipeline_core(
         - Optimized hyperparameters.
 
     """
-    if pj.train_horizons_minutes is not None:
-        horizons = pj.train_horizons_minutes
-
     if input_data.empty:
         raise InputDataInsufficientError("Input dataframe is empty")
     elif "load" not in input_data.columns:
diff --git a/openstef/pipeline/train_model.py b/openstef/pipeline/train_model.py
index 676cc8325..db7d72608 100644
--- a/openstef/pipeline/train_model.py
+++ b/openstef/pipeline/train_model.py
@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: MPL-2.0
 import logging
 import os
-from typing import Optional, Union, Tuple
+from typing import Optional, Union
 
 import pandas as pd
 import structlog
@@ -25,7 +25,7 @@
 from openstef.model_selection.model_selection import split_data_train_validation_test
 from openstef.validation import validation
 
-DEFAULT_TRAIN_HORIZONS: list[float] = [0.25, 47.0]
+DEFAULT_TRAIN_HORIZONS_HOURS: list[float] = [0.25, 47.0]
 MAXIMUM_MODEL_AGE: int = 7
 
 DEFAULT_EARLY_STOPPING_ROUNDS: int = 10
@@ -80,12 +80,19 @@ def train_model_pipeline(
 
     # Train model with core pipeline
     try:
+        if pj.train_horizons_minutes is None:
+            horizons = DEFAULT_TRAIN_HORIZONS_HOURS
+        else:
+            horizons = [
+                horizon_minutes / 60 for horizon_minutes in pj.train_horizons_minutes
+            ]
+
         model, report, model_specs_updated, data_sets = train_model_pipeline_core(
             pj,
             model_specs,
             input_data,
             old_model,
-            horizons=pj.train_horizons_minutes,
+            horizons=horizons,
         )
     except OldModelHigherScoreError as OMHSE:
         logger.error("Old model is better than new model", pid=pj["id"], exc_info=OMHSE)
@@ -134,7 +141,7 @@ def train_model_pipeline_core(
     model_specs: ModelSpecificationDataClass,
     input_data: pd.DataFrame,
     old_model: OpenstfRegressor = None,
-    horizons: Union[list[float], str] = None,
+    horizons: Union[list[float], str] = DEFAULT_TRAIN_HORIZONS_HOURS,
 ) -> Union[
     OpenstfRegressor,
     Report,
@@ -151,7 +158,7 @@ def train_model_pipeline_core(
         model_specs: Dataclass containing model specifications
         input_data: Input data
         old_model: Old model to compare to. Defaults to None.
-        horizons: horizons to train on in hours.
+        horizons: Horizons to train on in hours, relevant for feature engineering.
 
     Raises:
         InputDataInsufficientError: when input data is insufficient.
@@ -165,12 +172,6 @@ def train_model_pipeline_core(
         - Datasets (tuple[pd.DataFrmae, pd.DataFrame, pd.Dataframe): The train, validation and test sets
 
     """
-    if horizons is None:
-        if pj.train_horizons_minutes is None:
-            horizons = DEFAULT_TRAIN_HORIZONS
-        else:
-            horizons = pj.train_horizons_minutes
-
     logger = structlog.get_logger(__name__)
 
     # Call common pipeline