The race condition in the original code occurs because multiple threads can access and modify the shared dictionaries (`self.jobs` and `self.results`) simultaneously. Specifically:
1.  **Double Processing:** Two threads could check `if job_id not in self.jobs` at the same time, both find the job, and both proceed to process it.
2.  **Data Corruption:** Concurrent writes to `self.results` without synchronization can lead to undefined behavior or lost updates (though CPython's GIL often masks this, it is not thread-safe logic).

To fix this, we need to use a `threading.Lock` to protect the critical sections where shared state is accessed. We should also use the `pop` method to atomically retrieve and remove a job from the queue, ensuring that a job is processed by only one thread.

Here is the complete fixed implementation:

```python
import threading

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

    def add_job(self, job_id, data):
        # Lock to ensure thread-safe addition of jobs
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # We must lock the check-and-remove operation to prevent
        # multiple threads from picking the same job (double processing).
        with self.lock:
            if job_id not in self.jobs:
                return False
            # pop() removes the item from the dictionary atomically.
            # This claims the job for this thread so others won't take it.
            data = self.jobs.pop(job_id)
        
        # Perform the actual processing outside the lock.
        # This allows other threads to pick up and process other jobs
        # while this one is running, maximizing concurrency.
        result = processor(data)

        # Lock again to safely write the result to the shared dictionary
        with self.lock:
            self.results[job_id] = result

        return True

    def get_result(self, job_id):
        # Lock to safely read the result
        with self.lock:
            return self.results.get(job_id)
```

### Key Changes Made:
1.  **Added `self.lock = threading.Lock()`**: A lock object is created in the constructor to manage thread synchronization.
2.  **Protected `add_job`**: The dictionary write is wrapped in `with self.lock:` to prevent race conditions when multiple producers add jobs simultaneously.
3.  **Refactored `process_job`**:
    *   **Atomic Claiming**: The check for the job's existence and its removal from `self.jobs` are performed inside a lock block using `self.jobs.pop(job_id)`. This ensures that once a thread retrieves the data, the job is gone from the queue for other threads.
    *   **Processing Outside Lock**: The `processor(data)` function is called *outside* the lock. This is crucial for performance; holding a lock while executing a potentially long-running task would block all other threads from using the queue.
    *   **Protected Result Storage**: The write to `self.results` is wrapped in a lock to ensure thread-safe storage of the outcome.
4.  **Protected `get_result`**: The read access to `self.results` is synchronized.