Here is the fixed implementation.

### Analysis of the Bug
The original code has two primary concurrency issues:
1.  **Race Condition on `self.results`:** As noted in the comment, multiple threads can write to `self.results` simultaneously. While Python's GIL makes single dictionary assignments atomic, relying on this for logic consistency is bad practice, and compound operations are not atomic.
2.  **Race Condition on `self.jobs` (Check-Then-Act):** The `process_job` method checks if a job exists (`if job_id not in self.jobs`), retrieves it, and then deletes it. If two threads run this check at the same time, both might find the job, process it (duplicate work), and one might crash trying to delete a job the other already deleted.

### The Fix
We use `threading.Lock` to create a critical section. This ensures that only one thread can modify the shared dictionaries (`jobs` and `results`) at a time. We also use the dictionary `pop` method inside the lock to atomically check for existence and remove the job, preventing duplicate processing.

### Fixed Code

```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 required to safely add to the shared dictionary
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Lock to atomically check existence and remove the job.
        # Using 'pop' prevents another thread from picking up the same job.
        with self.lock:
            # pop returns the value if found, or the default (None) if not.
            # Note: If data can be validly None, a unique sentinel object should be used 
            # as the default to distinguish between "key missing" and "value is None".
            # Here we assume data is not None or handle it simply.
            data = self.jobs.pop(job_id, None)
            
            if data is None:
                # If data is None, we can't be sure if the key was missing 
                # or if the value was None. 
                # To be strictly correct with the original logic (which returns False only if key missing),
                # we should check existence first or use a sentinel. 
                # However, for standard usage, we assume valid data. 
                # A safer approach for strict logic:
                # return False 
                # But since we popped, the key is gone. 
                # Let's revert to a safer check-then-pop pattern for 100% correctness 
                # regarding the original False return behavior.
                pass 

        # --- REVISION FOR STRICT CORRECTNESS ---
        # The logic above with pop(None) is ambiguous if data=None is allowed. 
        # Let's use the explicit check inside the lock for absolute safety.
        
        # Let's rewrite the method body cleanly below.

    # ... (Implementing the clean version in the block below)
```

Here is the complete, clean, and correct implementation:

```python
import threading

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

    def add_job(self, job_id, data):
        # Lock the resource before modifying the dictionary
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Lock to safely check existence and remove the job atomically.
        # This prevents multiple threads from processing the same job_id.
        with self.lock:
            if job_id not in self.jobs:
                return False
            
            # Retrieve data and remove it from jobs in one step
            data = self.jobs.pop(job_id)
        
        # Perform the actual processing OUTSIDE the lock.
        # This allows other threads to add/process other jobs while this one runs.
        result = processor(data)

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

        return True

    def get_result(self, job_id):
        # Lock to ensure we read a consistent state of the results dictionary
        with self.lock:
            return self.results.get(job_id)
```