The concurrency bug in the original code arises from the "Check-Then-Act" race condition. Specifically, the check `if job_id not in self.jobs` and the subsequent removal `del self.jobs[job_id]` are not atomic. This allows multiple threads to see that a job exists, retrieve it, and process it simultaneously, or cause a `KeyError` when the second thread tries to delete a job already removed by the first. Additionally, writing to `self.results` without synchronization can lead to race conditions on the dictionary structure (though less critical in CPython due to the GIL, it is unsafe practice).

To fix this, we need to use a `threading.Lock` to protect the shared state (`self.jobs` and `self.results`). The most robust way to fix the queue logic is to atomically check for the job and remove it (using `pop`) while holding the lock, process the job outside the lock (to allow concurrency), and then store the result under the lock.

Here is the complete fixed implementation:

```python
import threading

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

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

    def process_job(self, job_id, processor):
        # Lock to safely check for the job and remove it from the queue.
        # We use pop() to atomically retrieve and remove the job.
        # This prevents multiple threads from processing the same job.
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs.pop(job_id)
        
        # Process the job outside the lock.
        # This allows other threads to pick up and process other jobs 
        # while this one is being processed.
        result = processor(data)
        
        # Lock to safely write the result to the results dictionary
        with self.lock:
            self.results[job_id] = result
            
        return True

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

### Key Changes:
1.  **Added `self.lock = threading.Lock()`**: A lock instance is created in `__init__` to manage access to shared data.
2.  **Protected `add_job`**: The dictionary assignment is wrapped in `with self.lock:`.
3.  **Atomic Check-and-Remove in `process_job`**: 
    *   The check `if job_id not in self.jobs` and the removal of the job are now performed inside a single lock block using `self.jobs.pop(job_id)`. 
    *   `pop()` removes the key and returns the value in one atomic operation, ensuring that no other thread can retrieve the same job ID while we are holding the lock.
4.  **Concurrency Optimization**: The actual processing (`processor(data)`) happens **outside** the lock. This ensures that while one job is being processed, other threads are not blocked from picking up new jobs from the queue.
5.  **Protected `get_result`**: Reads are synchronized to ensure data consistency.