The race condition exists because multiple threads can access and modify the shared dictionaries (`self.jobs` and `self.results`) simultaneously. Specifically, the sequence of checking for a job, processing it, writing the result, and deleting the job is not atomic.

To fix this, we need to introduce a `threading.Lock` to synchronize access to the shared state.

Here is the complete fixed implementation:

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        # Initialize a lock to protect shared state
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        # Lock is needed here to safely write to the shared dictionary
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # We must hold the lock for the entire critical section:
        # 1. Checking if the job exists
        # 2. Reading the data
        # 3. Writing the result
        # 4. Deleting the job
        # 
        # Note: Holding the lock during 'processor(data)' serializes the execution
        # of jobs. This is necessary in this specific implementation because the
        # job is deleted from self.jobs only *after* processing. If we released
        # the lock before processing, another thread could grab the same job_id,
        # leading to duplicate processing or a KeyError when trying to delete it.
        with self.lock:
            if job_id not in self.jobs:
                return False

            data = self.jobs[job_id]
            
            # Perform the work
            result = processor(data)
            
            # Update shared state atomically
            self.results[job_id] = result
            del self.jobs[job_id]
            
            return True

    def get_result(self, job_id):
        # Lock is needed here to safely read from the shared dictionary
        with self.lock:
            return self.results.get(job_id)
```

### Explanation of the Fix:

1.  **`self.lock = threading.Lock()`**: Added in `__init__` to create a synchronization primitive.
2.  **`with self.lock:`**: Used in all methods that access `self.jobs` or `self.results`. This ensures that only one thread can execute the critical section at a time.
3.  **Atomicity in `process_job`**: The logic `check -> read -> process -> write -> delete` is wrapped in a single lock.
    *   If we only locked the write/delete part (as hinted in the comment), two threads could still pass the `if job_id not in self.jobs` check simultaneously, process the same data, and one would crash with a `KeyError` when trying to delete a job already deleted by the other.
    *   By locking the whole block, we ensure that once a thread claims a job (checks it exists), no other thread can touch that job ID until the first thread is finished.

*Note: In a high-performance production environment, you might optimize this by atomically removing the job from `self.jobs` (using `pop`) at the start, releasing the lock, processing the data, and then re-acquiring the lock to save the result. However, that would change the behavior of the queue (making the job invisible while processing), so the lock-based approach above is the safest direct fix for the provided code.*