To fix the concurrency bug, we need to introduce a `threading.Lock` to ensure that operations on the shared dictionaries (`self.jobs` and `self.results`) are atomic.

The most robust fix involves:
1.  Initializing a lock in `__init__`.
2.  Wrapping dictionary accesses in `add_job` and `get_result` with the lock.
3.  In `process_job`, using the lock to safely check for the job's existence and remove it (using `pop`) before processing. This prevents race conditions where multiple threads might try to process the same job.
4.  Executing the processor outside the lock to allow concurrency (so one slow job doesn't block the entire queue), and then re-acquiring the lock to save the result.

Here is the complete fixed implementation:

```python
import threading

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

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

    def process_job(self, job_id, processor):
        # 1. Lock to safely check existence and remove the job.
        # Using pop() atomically removes the job from the queue, 
        # ensuring no other thread processes it while we hold the data.
        with self.lock:
            if job_id not in self.jobs:
                return False
            # pop() removes the item and returns it
            data = self.jobs.pop(job_id)
        
        # 2. Process the job outside the lock.
        # This allows other threads to add or process other jobs concurrently.
        # Note: If the processor raises an exception, the job is removed 
        # from the queue (consumed) but not saved to results.
        result = processor(data)

        # 3. Lock to safely store the result.
        with self.lock:
            self.results[job_id] = result

        return True

    def get_result(self, job_id):
        # Lock to ensure thread-safe reading from the results dictionary
        with self.lock:
            return self.results.get(job_id)
```

### Key Changes Explained:
*   **`self.lock = threading.Lock()`**: Added a lock object to synchronize access.
*   **`with self.lock:`**: Used context managers to automatically acquire and release the lock, ensuring it is released even if an exception occurs.
*   **Atomic Removal (`pop`)**: In `process_job`, we use `self.jobs.pop(job_id)` inside the lock. This atomically checks for the job, retrieves the data, and removes it from the queue in one step. This prevents the "Check-Then-Act" race condition where Thread A checks if a job exists, but Thread B processes and deletes it before Thread A can act.
*   **Concurrency Optimization**: By processing the job (`processor(data)`) *outside* the lock, we allow the queue to handle other jobs while one job is being computed. (Note: If the processor crashes, the job is lost from the queue, which is standard behavior for many queue implementations to prevent infinite retry loops on crashing tasks).