The race condition occurs because multiple threads can access and modify the shared dictionaries (`self.jobs` and `self.results`) simultaneously without synchronization. Specifically, two threads could check if a job exists, process it, and write the result at the same time, leading to data corruption or lost work.

To fix this, we introduce a `threading.Lock`. We should also optimize the code by releasing the lock while the processor runs (to avoid blocking other threads during CPU-intensive tasks), while ensuring the job is "claimed" (removed from the queue) before processing begins.

Here is the complete fixed implementation:

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self.lock = threading.Lock()  # Initialize the lock

    def add_job(self, job_id, data):
        # Protect the shared 'jobs' dictionary
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # 1. Claim the job atomically
        with self.lock:
            if job_id not in self.jobs:
                return False
            
            # Retrieve data and remove job from queue to claim it
            data = self.jobs[job_id]
            del self.jobs[job_id]
        
        # 2. Process the job outside the lock
        # (This allows other threads to access the queue while this job is being processed)
        try:
            result = processor(data)
            
            # 3. Store the result atomically
            with self.lock:
                self.results[job_id] = result
        except Exception:
            # Optional: Handle processor failures. 
            # Note: The job was already removed from self.jobs during 'claim',
            # so it will not be re-queued unless you implement retry logic here.
            pass
            
        return True

    def get_result(self, job_id):
        # Protect the shared 'results' dictionary
        with self.lock:
            return self.results.get(job_id)
```

### Key Changes Made:
1.  **Added `self.lock = threading.Lock()`**: This creates the mutex object required for synchronization.
2.  **Wrapped Dictionary Access**: All reads and writes to `self.jobs` and `self.results` are now protected by `with self.lock:`.
3.  **Optimized Lock Scope**: In `process_job`, the lock is held only during the "claim" phase (checking existence and deleting from `jobs`) and the "store" phase (writing to `results`). The actual `processor(data)` execution happens outside the lock. This prevents the queue from becoming a bottleneck if the processor performs CPU-intensive work.
4.  **Protected `add_job` and `get_result`**: These methods also access shared state and now utilize the lock to ensure consistency.